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Complex-Object Visual Inspection: Empirical Studies on a Multiple Lighting Solution

Maya Aghaei, Matteo Bustreo, Pietro Morerio, Nicolò Carissimi, Alessio Del Bue, Vittorio Murino

Auto-TLDR; A Novel Illumination Setup for Automatic Visual Inspection of Complex Objects

Abstract Slides Poster

The design of an automatic visual inspection system is usually performed in two stages. While the first stage consists in selecting the most suitable hardware setup for highlighting most effectively the defects on the surface to be inspected, the second stage concerns the development of algorithmic solutions to exploit the potentials offered by the collected data. In this paper, first, we present a novel illumination setup embedding four illumination configurations to resemble diffused, dark-field, and front lighting techniques. Second, we analyze the contributions brought by deploying the proposed setup in the training phase only, mimicking the scenario in which an already developed visual inspection system cannot be modified on the customer site. Along with an exhaustive set of experiments, in this paper, we demonstrate the suitability of the proposed setup for effective illumination of complex-objects, defined as manufactured items with variable surface characteristics that cannot be determined a priori. Eventually, we provide insights into the importance of multiple light configurations availability during training and their natural boosting effect which, without the need to modify the system design in the evaluation phase, lead to improvements in the overall system performance.

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A Versatile Crack Inspection Portable System Based on Classifier Ensemble and Controlled Illumination

Milind Gajanan Padalkar, Carlos Beltran-Gonzalez, Matteo Bustreo, Alessio Del Bue, Vittorio Murino

Auto-TLDR; Lighting Conditions for Crack Detection in Ceramic Tile

Abstract Slides Poster Similar

This paper presents a novel setup for automatic visual inspection of cracks in ceramic tile as well as studies the effect of various classifiers and height-varying illumination conditions for this task. The intuition behind this setup is that cracks can be better visualized under specific lighting conditions than others. Our setup, which is designed for field work with constraints in its maximum dimensions, can acquire images for crack detection with multiple lighting conditions using the illumination sources placed at multiple heights. Crack detection is then performed by classifying patches extracted from the acquired images in a sliding window fashion. We study the effect of lights placed at various heights by training classifiers both on customized as well as state-of-the-art architectures and evaluate their performance both at patch-level and image-level, demonstrating the effectiveness of our setup. More importantly, ours is the first study that demonstrates how height-varying illumination conditions can affect crack detection with the use of existing state-of-the-art classifiers. We provide an insight about the illumination conditions that can help in improving crack detection in a challenging real-world industrial environment.

ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surface Defect Detection

Junting Fang, Xiaoyang Tan, Yuhui Wang

Auto-TLDR; Attention Cascade R-CNN with Mix Non-Maximum Suppression for Robust Metal Defect Detection

Abstract Slides Poster Similar

Metallic surface defect detection is of great significance in quality control for production. However, this task is very challenging due to the noise disturbance, large appearance variation, and the ambiguous definition of the defect individual. Traditional image processing methods are unable to detect the damaged region effectively and efficiently. In this paper, we propose a new defect detection method, Attention Cascade R-CNN with Mix-NMS (ACRM), to classify and locate defects robustly. Three submodules are developed to achieve this goal: 1) a lightweight attention block is introduced, which can improve the ability in capture global and local feature both in the spatial and channel dimension; 2) we firstly apply the cascade R-CNN to our task, which exploits multiple detectors to sequentially refine the detection result robustly; 3) we introduce a new method named Mix Non-Maximum Suppression (Mix-NMS), which can significantly improve its ability in filtering the redundant detection result in our task. Extensive experiments on a real industrial dataset show that ACRM achieves state-of-the-art results compared to the existing methods, demonstrating the effectiveness and robustness of our detection method.

IPT: A Dataset for Identity Preserved Tracking in Closed Domains

Thomas Heitzinger, Martin Kampel

Auto-TLDR; Identity Preserved Tracking Using Depth Data for Privacy and Privacy

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We present a public dataset for Identity Preserved Tracking (IPT) consisting of sequences of depth data recorded using an Orbbec Astra depth sensor. The dataset features sequences in ten different locations with a high amount of background variation and is designed to be applicable to a wide range of tasks. Its labeling is versatile, allowing for tracking in either 3d space or image coordinates. Next to frame-by-frame 3d and inferred bounding box labeling we provide supplementary annotation of camera poses and room layouts, split in multiple semantically distinct categories. Intended use-cases are applications where both a high level understanding of scene understanding and privacy are central points of consideration, such as active and assisted living (AAL), security and industrial safety. Compared to similar public datasets IPT distinguishes itself with its sequential data format, 3d instance labeling and room layout annotation. We present baseline object detection results in image coordinates using a YOLOv3 network architecture and implement a background model suitable for online tracking applications to increase detection accuracy. Additionally we propose a novel volumetric non-maximum suppression (V-NMS) approach, taking advantage of known room geometry. Last we provide baseline person tracking results utilizing Multiple Object Tracking Challenge (MOTChallenge) evaluation metrics of the CVPR19 benchmark.

End-To-End Training of a Two-Stage Neural Network for Defect Detection

Jakob Božič, Domen Tabernik, Danijel Skocaj

Auto-TLDR; End-to-End Training of Segmentation-based Neural Network for Surface Defect Detection

Abstract Slides Poster Similar

Segmentation-based, two-stage neural network has shown excellent results in the surface defect detection, enabling the network to learn from a relatively small number of samples. In this work, we introduce end-to-end training of the two-stage network together with several extensions to the training process, which reduce the amount of training time and improve results on surface defect detection tasks. To enable end-to-end training we carefully balance the contributions of both the segmentation and the classification loss throughout the learning. We adjust the gradient flow from the classification into the segmentation network in order to prevent the unstable features from corrupting the learning. As additional extension to the learning, we propose frequency-of-use sampling scheme of negative samples to address the issue of over- and under-sampling of images during the training, while we employ the distance transform algorithm on the region-based segmentation masks as weights for positive pixels, giving greater importance to areas with higher probability of presence of defect without requiring a detailed annotation. We demonstrate the performance of the end-to-end training scheme and the proposed extensions on three defect detection datasets---DAGM, KolektorSDD and Severstal Steel defect dataset--- where we show state-of-the-art results. On the DAGM and the KolektorSDD we demonstrate 100\% detection rate, therefore completely solving the datasets. Additional ablation study performed on all three datasets quantitatively demonstrates the contribution to the overall result improvements for each of the proposed extensions.

Uncertainty Guided Recognition of Tiny Craters on the Moon

Thorsten Wilhelm, Christian Wöhler

Auto-TLDR; Accurately Detecting Tiny Craters in Remote Sensed Images Using Deep Neural Networks

Abstract Slides Poster Similar

Accurately detecting craters in remotely sensed images is an important task when analysing the properties of planetary bodies. Commonly, only large craters in the range of several kilometres are detected. In this work we provide the first example of automatically detecting tiny craters in the range of several meters with the help of a deep neural network by using only a small set of annotated craters. Additionally, we propose a novel way to group overlapping detections and replace the commonly used non-maximum suppression with a probabilistic treatment. As a result, we receive valuable uncertainty estimates of the detections and the aggregated detections are shown to be vastly superior.

Anomaly Detection, Localization and Classification for Railway Inspection

Riccardo Gasparini, Andrea D'Eusanio, Guido Borghi, Stefano Pini, Giuseppe Scaglione, Simone Calderara, Eugenio Fedeli, Rita Cucchiara

Auto-TLDR; Anomaly Detection and Localization using thermal images in the lowlight environment

Abstract Slides Similar

The ability to detect, localize and classify objects that are anomalies is a challenging task in the computer vision community. In this paper, we tackle these tasks developing a framework to automatically inspect the railway during the night. Specifically, it is able to predict the presence, the image coordinates and the class of obstacles. To deal with the lowlight environment, the framework is based on thermal images and consists of three different modules that address the problem of detecting anomalies, predicting their image coordinates and classifying them. Moreover, due to the absolute lack of publicly released datasets collected in the railway context for anomaly detection, we introduce a new multi-modal dataset, acquired from a rail drone, used to evaluate the proposed framework. Experimental results confirm the accuracy of the framework and its suitability, in terms of computational load, performance, and inference time, to be implemented on a self-powered inspection system.

Construction Worker Hardhat-Wearing Detection Based on an Improved BiFPN

Chenyang Zhang, Zhiqiang Tian, Jingyi Song, Yaoyue Zheng, Bo Xu

Auto-TLDR; A One-Stage Object Detection Method for Hardhat-Wearing in Construction Site

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Work in the construction site is considered to be one of the occupations with the highest safety risk factor. Therefore, safety plays an important role in construction site. One of the most fundamental safety rules in construction site is to wear a hardhat. To strengthen the safety of the construction site, most of the current methods use multi-stage method for hardhat-wearing detection. These methods have limitations in terms of adaptability and generalizability. In this paper, we propose a one-stage object detection method based on convolutional neural network. We present a multi-scale strategy that selects the high-resolution feature maps of DarkNet-53 to effectively identify small-scale hardhats. In addition, we propose an improved weighted bi-directional feature pyramid network (BiFPN), which could fuse more semantic features from more scales. The proposed method can not only detect hardhat-wearing, but also identify the color of the hardhat. Experimental results show that the proposed method achieves a mAP of 87.04%, which outperforms several state-of-the-art methods on a public dataset.

End-To-End Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales

Yongsheng Bai, Alper Yilmaz, Halil Sezen

Auto-TLDR; Robust Mask R-CNN for Crack Detection in Extreme Events

Abstract Slides Poster Similar

Robust Mask R-CNN (Mask Regional Convolutional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes. We curated a new dataset with 2,021 labeled images for training and validation and aimed to find end-to-end deep neural networks for crack detection in the field. With data augmentation and parameters fine-tuning, Path Aggregation Network (PANet) with spatial attention mechanisms and High-resolution Network (HRNet) are introduced into Mask R-CNNs. The tests on three public datasets with low- or high-resolution images demonstrate that the proposed methods can achieve a big improvement over alternative networks, so the proposed method may be sufficient for crack detection for a variety of scales in real applications.

Real-Time Drone Detection and Tracking with Visible, Thermal and Acoustic Sensors

Fredrik Svanström, Cristofer Englund, Fernando Alonso-Fernandez

Auto-TLDR; Automatic multi-sensor drone detection using sensor fusion

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This paper explores the process of designing an automatic multi-sensor drone detection system. Besides the common video and audio sensors, the system also includes a thermal infrared camera, which is shown to be a feasible solution to the drone detection task. Even with slightly lower resolution, the performance is just as good as a camera in visible range. The detector performance as a function of the sensor-to-target distance is also investigated. In addition, using sensor fusion, the system is made more robust than the individual sensors, helping to reduce false detections. To counteract the lack of public datasets, a novel video dataset containing 650 annotated infrared and visible videos of drones, birds, airplanes and helicopters is also presented. The database is complemented with an audio dataset of the classes drones, helicopters and background noise.

Multi-View Object Detection Using Epipolar Constraints within Cluttered X-Ray Security Imagery

Brian Kostadinov Shalon Isaac-Medina, Chris G. Willcocks, Toby Breckon

Auto-TLDR; Exploiting Epipolar Constraints for Multi-View Object Detection in X-ray Security Images

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Automatic detection for threat object items is an increasing emerging area of future application in X-ray security imagery. Although modern X-ray security scanners can provide two or more views, the integration of such object detectors across the views has not been widely explored with rigour. Therefore, we investigate the application of geometric constraints using the epipolar nature of multi-view imagery to improve object detection performance. Furthermore, we assume that images come from uncalibrated views, such that a method to estimate the fundamental matrix using ground truth bounding box centroids from multiple view object detection labels is proposed. In addition, detections are given a score based on its similarity with respect to the distribution of the error of the epipolar estimation. This score is used as confidence weights for merging duplicated predictions using non-maximum suppression. Using a standard object detector (YOLOv3), our technique increases the average precision of detection by 2.8% on a dataset composed of firearms, laptops, knives and cameras. These results indicate that the integration of images at different views significantly improves the detection performance of threat items of cluttered X-ray security images.

Evaluation of Anomaly Detection Algorithms for the Real-World Applications

Marija Ivanovska, Domen Tabernik, Danijel Skocaj, Janez Pers

Auto-TLDR; Evaluating Anomaly Detection Algorithms for Practical Applications

Abstract Slides Poster Similar

Anomaly detection in complex data structures is oneof the most challenging problems in computer vision. In manyreal-world problems, for example in the quality control in modernmanufacturing, the anomalous samples are usually rare, resultingin (highly) imbalanced datasets. However, in current researchpractice, these scenarios are rarely modeled, and as a conse-quence, evaluation of anomaly detection algorithms often do notreproduce results that are useful for practical applications. First,even in case of highly unbalanced input data, anomaly detectionalgorithms are expected to significantly reduce the proportionof anomalous samples, detecting ”almost all” anomalous samples(with exact specifications depending on the target customer). Thisplaces high importance on only the small part of the ROC curve,possibly rendering the standard metrics such as AUC (AreaUnder Curve) and AP (Average Precision) useless. Second, thetarget of automatic anomaly detection in practical applicationsis significant reduction in manual work required, and standardmetrics are poor predictor of this feature. Finally, the evaluationmay produce erratic results for different randomly initializedtraining runs of the neural network, producing evaluation resultsthat may not reproduce well in practice. In this paper, we presentan evaluation methodology that avoids these pitfalls.

FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings

Niels Ole Salscheider

Auto-TLDR; FeatureNMS: Non-Maximum Suppression for Multiple Object Detection

Abstract Slides Poster Similar

Most state of the art object detectors output multiple detections per object. The duplicates are removed in a post-processing step called Non-Maximum Suppression. Classical Non-Maximum Suppression has shortcomings in scenes that contain objects with high overlap: The idea of this heuristic is that a high bounding box overlap corresponds to a high probability of having a duplicate. We propose FeatureNMS to solve this problem. FeatureNMS recognizes duplicates not only based on the intersection over union between bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance. Our approach outperforms classical NMS and derived approaches and achieves state of the art performance.

TGCRBNW: A Dataset for Runner Bib Number Detection (and Recognition) in the Wild

Pablo Hernández-Carrascosa, Adrian Penate-Sanchez, Javier Lorenzo, David Freire Obregón, Modesto Castrillon

Auto-TLDR; Racing Bib Number Detection and Recognition in the Wild Using Faster R-CNN

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Racing bib number (RBN) detection and recognition is a specific problem related to text recognition in natural scenes. In this paper, we present a novel dataset created after registering participants in a real ultrarunning competition which comprises a wide range of acquisition conditions in five different recording points, including nightlight and daylight. The dataset contains more than 3k samples of over 400 different individuals. The aim is at providing an in the wild benchmark for both RBN detection and recognition problems. To illustrate the present difficulties, the dataset is evaluated for RBN detection using different Faster R-CNN specific detection models, filtering its output with heuristics based on body detection to improve the overall detection performance. Initial results are promising, but there is still a significant room for improvement. And detection is just the first step to accomplish in the wild RBN recognition.

Crack Detection As a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors

Yuki Inoue, Hiroto Nagayoshi

Auto-TLDR; A Weakly-supervised Framework for Automatic Crack Detection

Abstract Slides Poster Similar

Automatic crack detection is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually. Recent studies in this field have made significant improvements on detection accuracy. However, they often require costly annotation processes. In addition, to handle a wide variety of target domains, new batches of annotations are usually required for each new environment. This makes the data annotation cost a significant bottleneck when deploying crack detection systems. To resolve this issue, we formulate the crack detection problem as a weakly-supervised problem and propose a two-branched framework. By combining predictions from a supervised model trained with low quality annotations and predictions from pixel intensities, our framework is less affected by annotation quality. Experimental results show that the proposed framework retains a high accuracy even when provided with lower quality annotations.

Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings

Daniele De Gregorio, Riccardo Zanella, Gianluca Palli, Luigi Di Stefano

Auto-TLDR; Automated Deep Learning for Robotic Grasping Applications

Abstract Slides Poster Similar

In this paper we investigate how to effectively deploy deep learning in practical industrial settings, such as robotic grasping applications. When a deep-learning based solution is proposed, usually lacks of any simple method to generate the training data. In the industrial field, where automation is the main goal, not bridging this gap is one of the main reasons why deep learning is not as widespread as it is in the academic world. For this reason, in this work we developed a system composed by a 3-DoF Pose Estimator based on Convolutional Neural Networks (CNNs) and an effective procedure to gather massive amounts of training images in the field with minimal human intervention. By automating the labeling stage, we also obtain very robust systems suitable for production-level usage. An open source implementation of our solution is provided, alongside with the dataset used for the experimental evaluation.

Polarimetric Image Augmentation

Marc Blanchon, Fabrice Meriaudeau, Olivier Morel, Ralph Seulin, Desire Sidibe

Auto-TLDR; Polarimetric Augmentation for Deep Learning in Robotics Applications

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This paper deals with new augmentation methods for an unconventional imaging modality sensitive to the physics of the observed scene called polarimetry. In nature, polarized light is obtained by reﬂection or scattering. Robotics applications in urban environments are subject to many obstacles that can be specular and therefore provide polarized light. These areas are prone to segmentation errors using standard modalities but could be solved using information carried by the polarized light. Deep Convolutional Neural Networks (DCNNs) have shown excellent segmentation results, but require a signiﬁcant amount of data to achieve best performances. The lack of data is usually overcomed by using augmentation methods. However, unlike RGB images, polarization images are not only scalar (intensity) images and standard augmentation techniques cannot be applied straightforwardly. We propose enhancing deep learning models through a regularized augmentation procedure applied to polarimetric data in order to characterize scenes more effectively under challenging conditions. We subsequently observe an average of 18.1% improvement in IoU between not augmented and regularized training procedures on real world data.

Mobile Phone Surface Defect Detection Based on Improved Faster R-CNN

Tao Wang, Can Zhang, Runwei Ding, Ge Yang

Auto-TLDR; Faster R-CNN for Mobile Phone Surface Defect Detection

Abstract Slides Poster Similar

Various surface defects will inevitably occur in the production process of mobile phones, which have a huge impact on the enterprise. Therefore, precise defect detection is of great significance in the production of mobile phones. However, the traditional manual inspection and machine vision inspection have low efficiency and accuracy respectively which cannot meet the rapid production needs of modern enterprises. In this paper, we proposed a mobile phone surface defect (MPSD) detection model based on deep learning, which greatly reduce the requirement of a large dataset and improve detection performance. First, Boundary Equilibrium Generative Adversarial Networks (BEGAN) is used to generate and augment the defect data. Then, based on Faster R-CNN model, Feature Pyramid Network (FPN) and ResNet 101 are combined as feature extraction network to get more small target defect features. Further, replacing the ROI pooling layer with an ROI Align layer reduces the quantization deviation during the pooling process. Finally, we train and evaluate our model on our own dataset. The experimental results indicate that compared with some traditional methods based on handcrafted feature extraction and the traditional Faster R-CNN, the improved Faster R-CNN achieves 99.43% mAP, which is more effective in MPSD defect detection area.

Early Wildfire Smoke Detection in Videos

Taanya Gupta, Hengyue Liu, Bir Bhanu

Auto-TLDR; Semi-supervised Spatio-Temporal Video Object Segmentation for Automatic Detection of Smoke in Videos during Forest Fire

Abstract Poster Similar

Recent advances in unmanned aerial vehicles and camera technology have proven useful for the detection of smoke that emerges above the trees during a forest fire. Automatic detection of smoke in videos is of great interest to Fire department. To date, in most parts of the world, the fire is not detected in its early stage and generally it turns catastrophic. This paper introduces a novel technique that integrates spatial and temporal features in a deep learning framework using semi-supervised spatio-temporal video object segmentation and dense optical flow. However, detecting this smoke in the presence of haze and without the labeled data is difficult. Considering the visibility of haze in the sky, a dark channel pre-processing method is used that reduces the amount of haze in video frames and consequently improves the detection results. Online training is performed on a video at the time of testing that reduces the need for ground-truth data. Tests using the publicly available video datasets show that the proposed algorithms outperform previous work and they are robust across different wildfire-threatened locations.

Detecting Marine Species in Echograms Via Traditional, Hybrid, and Deep Learning Frameworks

Porto Marques Tunai, Alireza Rezvanifar, Melissa Cote, Alexandra Branzan Albu, Kaan Ersahin, Todd Mudge, Stephane Gauthier

Auto-TLDR; End-to-End Deep Learning for Echogram Interpretation of Marine Species in Echograms

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This paper provides a comprehensive comparative study of traditional, hybrid, and deep learning (DL) methods for detecting marine species in echograms. Acoustic backscatter data obtained from multi-frequency echosounders is visualized as echograms and typically interpreted by marine biologists via manual or semi-automatic methods, which are time-consuming. Challenges related to automatic echogram interpretation are the variable size and acoustic properties of the biological targets (marine life), along with significant inter-class similarities. Our study explores and compares three types of approaches that cover the entire range of machine learning methods. Based on our experimental results, we conclude that an end-to-end DL-based framework, that can be readily scaled to accommodate new species, is overall preferable to other learning approaches for echogram interpretation, even when only a limited number of annotated training samples is available.

Dynamic Low-Light Image Enhancement for Object Detection Via End-To-End Training

Haifeng Guo, Yirui Wu, Tong Lu

Auto-TLDR; Object Detection using Low-Light Image Enhancement for End-to-End Training

Abstract Slides Poster Similar

Object detection based on convolutional neural networks is a hot research topic in computer vision. The illumination component in the image has a great impact on object detection, and it will cause a sharp decline in detection performance under low-light conditions. Using low-light image enhancement technique as a pre-processing mechanism can improve image quality and obtain better detection results.However, due to the complexity of low-light environments, the existing enhancement methods may have negative effects on some samples. Therefore, it is difficult to improve the overall detection performance in low-light conditions. In this paper, our goal is to use image enhancement to improve object detection performance rather than perceptual quality for humans. We propose a novel framework that combines low-light enhancement and object detection for end-to-end training. The framework can dynamically select different enhancement subnetworks for each sample to improve the performance of the detector. Our proposed method consists of two stage: the enhancement stage and the detection stage. The enhancement stage dynamically enhances the low-light images under the supervision of several enhancement methods and output corresponding weights. During the detection stage, the weights offers information on object classification to generate high-quality region proposals and in turn result in accurate detection. Our experiments present promising results, which show that the proposed method can significantly improve the detection performance in low-light environment.

One Step Clustering Based on A-Contrario Framework for Detection of Alterations in Historical Violins

Alireza Rezaei, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Piercarlo Dondi, Marco Malagodi

Auto-TLDR; A-Contrario Clustering for the Detection of Altered Violins using UVIFL Images

Abstract Slides Poster Similar

Preventive conservation is an important practice in Cultural Heritage. The constant monitoring of the state of conservation of an artwork helps us reduce the risk of damage and number of interventions necessary. In this work, we propose a probabilistic approach for the detection of alterations on the surface of historical violins based on an a-contrario framework. Our method is a one step NFA clustering solution which considers grey-level and spatial density information in one background model. The proposed method is robust to noise and avoids parameter tuning and any assumption about the quantity of the worn out areas. We have used as input UV induced fluorescence (UVIFL) images for considering details not perceivable with visible light. Tests were conducted on image sequences included in the ``Violins UVIFL imagery'' dataset. Results illustrate the ability of the algorithm to distinguish the worn area from the surrounding regions. Comparisons with the state of the art clustering methods shows improved overall precision and recall.

A Systematic Investigation on Deep Architectures for Automatic Skin Lesions Classification

Pierluigi Carcagni, Marco Leo, Andrea Cuna, Giuseppe Celeste, Cosimo Distante

Auto-TLDR; RegNet: Deep Investigation of Convolutional Neural Networks for Automatic Classification of Skin Lesions

Abstract Slides Poster Similar

Computer vision-based techniques are more and more employed in healthcare and medical fields nowadays in order, principally, to be as a support to the experienced medical staff to help them to make a quick and correct diagnosis. One of the hot topics in this arena concerns the automatic classification of skin lesions. Several promising works exist about it, mainly leveraging Convolutional Neural Networks (CNN), but proposed pipeline mainly rely on complex data preprocessing and there is no systematic investigation about how available deep models can actually reach the accuracy needed for real applications. In order to overcome these drawbacks, in this work, an end-to-end pipeline is introduced and some of the most recent Convolutional Neural Networks (CNNs) architectures are included in it and compared on the largest common benchmark dataset recently introduced. To this aim, for the first time in this application context, a new network design paradigm, namely RegNet, has been exploited to get the best models among a population of configurations. The paper introduces a threefold level of contribution and novelty with respect the previous literature: the deep investigation of several CNN architectures driving to a consistent improvement of the lesions recognition accuracy, the exploitation of a new network design paradigm able to study the behavior of populations of models and a deep discussion about pro and cons of each analyzed method paving the path towards new research lines.

DR2S: Deep Regression with Region Selection for Camera Quality Evaluation

Marcelin Tworski, Stéphane Lathuiliere, Salim Belkarfa, Attilio Fiandrotti, Marco Cagnazzo

Auto-TLDR; Texture Quality Estimation Using Deep Learning

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In this work, we tackle the problem of estimating a camera capability to preserve fine texture details at a given lighting condition. Importantly, our texture preservation measurement should coincide with human perception. Consequently, we formulate our problem as a regression one and we introduce a deep convolutional network to estimate texture quality score. At training time, we use ground-truth quality scores provided by expert human annotators in order to obtain a subjective quality measure. In addition, we propose a region selection method to identify the image regions that are better suited at measuring perceptual quality. Finally, our experimental evaluation shows that our learning-based approach outperforms existing methods and that our region selection algorithm consistently improves the quality estimation.

Weight Estimation from an RGB-D Camera in Top-View Configuration

Marco Mameli, Marina Paolanti, Nicola Conci, Filippo Tessaro, Emanuele Frontoni, Primo Zingaretti

Auto-TLDR; Top-View Weight Estimation using Deep Neural Networks

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The development of so-called soft-biometrics aims at providing information related to the physical and behavioural characteristics of a person. This paper focuses on bodyweight estimation based on the observation from a top-view RGB-D camera. In fact, the capability to estimate the weight of a person can be of help in many different applications, from health-related scenarios to business intelligence and retail analytics. To deal with this issue, a TVWE (Top-View Weight Estimation) framework is proposed with the aim of predicting the weight. The approach relies on the adoption of Deep Neural Networks (DNNs) that have been trained on depth data. Each network has also been modified in its top section to replace classification with prediction inference. The performance of five state-of-art DNNs has been compared, namely VGG16, ResNet, Inception, DenseNet and Efficient-Net. In addition, a convolutional auto-encoder has also been included for completeness. Considering the limited literature in this domain, the TVWE framework has been evaluated on a new publicly available dataset: “VRAI Weight estimation Dataset”, which also collects, for each subject, labels related to weight, gender, and height. The experimental results have demonstrated that the proposed methods are suitable for this task, bringing different and significant insights for the application of the solution in different domains.

Which Airline Is This? Airline Logo Detection in Real-World Weather Conditions

Christian Wilms, Rafael Heid, Mohammad Araf Sadeghi, Andreas Ribbrock, Simone Frintrop

Auto-TLDR; Airlines logo detection on airplane tails using data augmentation

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The detection of logos in images, for instance, logos of airlines on airplane tails, is a difficult task in real-world weather conditions. Most systems used for logo detection are very good at detecting logos in clean images. However, they exhibit problems when images are degraded by effects of adverse weather conditions as they frequently occur in real-world scenarios. For investigating this problem on airline logo detection as a sub-problem of logo detection, we first present a new dataset for airline logo detection on airplane tails containing a test split with images degraded by adverse weather effects. Second, to handle the detection of airline logos effectively, a new two-stage airline logo detection system based on a state-of-the-art object proposal generation system and a specifically tailored classifier is proposed. Finally, improving the results on images degraded by adverse weather effects, we introduce a learning-free application-agnostic data augmentation strategy simulating effects like rain and fog. The results show the superior performance of our airline logo detection system compared to state-of-the-art. Furthermore, applying our data augmentation approach to a variety of systems, reduces the significant drop in performance on degraded images.

A Systematic Investigation on End-To-End Deep Recognition of Grocery Products in the Wild

Marco Leo, Pierluigi Carcagni, Cosimo Distante

Auto-TLDR; Automatic Recognition of Products on grocery shelf images using Convolutional Neural Networks

Abstract Slides Poster Similar

Automatic recognition of products on grocery shelf images is a new and attractive topic in computer vision and machine learning since, it can be exploited in different application areas. This paper introduces a complete end-to-end pipeline (without preliminary radiometric and spatial transformations usually involved while dealing with the considered issue) and it provides a systematic investigation of recent machine learning models based on convolutional neural networks for addressing the product recognition task by exploiting the proposed pipeline on a recent challenging grocery product dataset. The investigated models were never been used in this context: they derive from the successful and more generic object recognition task and have been properly tuned to address this specific issue. Besides, also ensembles of nets built by most advanced theoretical fundaments have been taken into account. Gathered classification results were very encouraging since the recognition accuracy has been improved up to 15\% with respect to the leading approaches in the state of art on the same dataset. A discussion about the pros and cons of the investigated solutions are discussed by paving the path towards new research lines.

Learning Defects in Old Movies from Manually Assisted Restoration

Arthur Renaudeau, Travis Seng, Axel Carlier, Jean-Denis Durou, Fabien Pierre, Francois Lauze, Jean-François Aujol

Auto-TLDR; U-Net: Detecting Defects in Old Movies by Inpainting Techniques

Abstract Slides Poster Similar

We propose to detect defects in old movies, as the first step of a larger framework of old movies restoration by inpainting techniques. The specificity of our work is to learn a film restorer's expertise from a pair of sequences, composed of a movie with defects, and the same movie which was semi-automatically restored with the help of a specialized software. In order to detect those defects with minimal human interaction and further reduce the time spent for a restoration, we feed a U-Net with consecutive defective frames as input to detect the unexpected variations of pixel intensity over space and time. Since the output of the network is a mask of defect location, we first have to create the dataset of mask frames on the basis of restored frames from the software used by the film restorer, instead of classical synthetic ground truth, which is not available. These masks are estimated by computing the absolute difference between restored frames and defectuous frames, combined with thresholding and morphological closing. Our network succeeds in automatically detecting real defects with more precision than the manual selection with an all-encompassing shape, including some the expert restorer could have missed for lack of time.

Automatically Gather Address Specific Dwelling Images Using Google Street View

Salman Khan, Carl Salvaggio

Auto-TLDR; Automatic Address Specific Dwelling Image Collection Using Google Street View Data

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Exciting research is being conducted using Google’s street view imagery. Researchers can have access to training data that allows CNN training for topics ranging from assessing neighborhood environments to estimating the age of a building. However, due to the uncontrolled nature of imagery available via Google’s Street View API, data collection can be lengthy and tedious. In an effort to help researchers gather address specific dwelling images efficiently, we developed an innovative and novel way of automatically performing this task. It was accomplished by exploiting Google’s publicly available platform with a combination of 3 separate network types and postprocessing techniques. Our uniquely developed NMS technique helped achieve 99.4%, valid, address specific dwelling images.

An Integrated Approach of Deep Learning and Symbolic Analysis for Digital PDF Table Extraction

Mengshi Zhang, Daniel Perelman, Vu Le, Sumit Gulwani

Auto-TLDR; Deep Learning and Symbolic Reasoning for Unstructured PDF Table Extraction

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Deep learning has shown great success at interpreting unstructured data such as object recognition in images. Symbolic/logical-reasoning techniques have shown great success in interpreting structured data such as table extraction in webpages, custom text files, spreadsheets. The tables in PDF documents are often generated from such structured sources (text-based Word/Latex documents, spreadsheets, webpages) but end up being unstructured. We thus explore novel combinations of deep learning and symbolic reasoning techniques to build an effective solution for PDF table extraction. We evaluate effectiveness without granting partial credit for matching part of a table (which may cause silent errors in downstream data processing). Our method achieves a 0.725 F1 score (vs. 0.339 for the state-of-the-art) on detecting correct table bounds---a much stricter metric than the common one of detecting characters within tables---in a well known public benchmark (ICDAR 2013) and a 0.404 F1 score (vs. 0.144 for the state-of-the-art) on our private benchmark with more widely varied table structures.

Semi-Supervised Deep Learning Techniques for Spectrum Reconstruction

Adriano Simonetto, Vincent Parret, Alexander Gatto, Piergiorgio Sartor, Pietro Zanuttigh

Auto-TLDR; hyperspectral data estimation from RGB data using semi-supervised learning

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State-of-the-art approaches for the estimation of hyperspectral images (HSI) from RGB data are mostly based on deep learning techniques but due to the lack of training data their performances are limited to uncommon scenarios where a large hyperspectral database is available. In this work we present a family of novel deep learning schemes for hyperspectral data estimation able to work when the hyperspectral information at our disposal is limited. Firstly, we introduce a learning scheme exploiting a physical model based on the backward mapping to the RGB space and total variation regularization that can be trained with a limited amount of HSI images. Then, we propose a novel semi-supervised learning scheme able to work even with just a few pixels labeled with hyperspectral information. Finally, we show that the approach can be extended to a transfer learning scenario. The proposed techniques allow to reach impressive performances while requiring only some HSI images or just a few pixels for the training.

A Novel Region of Interest Extraction Layer for Instance Segmentation

Leonardo Rossi, Akbar Karimi, Andrea Prati

Auto-TLDR; Generic RoI Extractor for Two-Stage Neural Network for Instance Segmentation

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Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extract a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. This paper is motivated by the need to overcome to the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP on bounding box detection and 1.7% AP on instance segmentation. The code is publicly available on GitHub repository at https://github.com/IMPLabUniPr/mmdetection-groie

The Color Out of Space: Learning Self-Supervised Representations for Earth Observation Imagery

Stefano Vincenzi, Angelo Porrello, Pietro Buzzega, Marco Cipriano, Pietro Fronte, Roberto Cuccu, Carla Ippoliti, Annamaria Conte, Simone Calderara

Auto-TLDR; Satellite Image Representation Learning for Remote Sensing

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The recent growth in the number of satellite images fosters the development of effective deep-learning techniques for Remote Sensing (RS). However, their full potential is untapped due to the lack of large annotated datasets. Such a problem is usually countered by fine-tuning a feature extractor that is previously trained on the ImageNet dataset. Unfortunately, the domain of natural images differs from the RS one, which hinders the final performance. In this work, we propose to learn meaningful representations from satellite imagery, leveraging its high-dimensionality spectral bands to reconstruct the visible colors. We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor. Furthermore, we qualitatively observe that guesses based on natural images and colorization rely on different parts of the input. This paves the way to an ensemble model that eventually outperforms both the above-mentioned techniques.

Automatic Classification of Human Granulosa Cells in Assisted Reproductive Technology Using Vibrational Spectroscopy Imaging

Marina Paolanti, Emanuele Frontoni, Giorgia Gioacchini, Giorgini Elisabetta, Notarstefano Valentina, Zacà Carlotta, Carnevali Oliana, Andrea Borini, Marco Mameli

Auto-TLDR; Predicting Oocyte Quality in Assisted Reproductive Technology Using Machine Learning Techniques

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In the field of reproductive technology, the biochemical composition of female gametes has been successfully investigated with the use of vibrational spectroscopy. Currently, in assistive reproductive technology (ART), there are no shared criteria for the choice of oocyte, and automatic classification methods for the best quality oocytes have not yet been applied. In this paper, considering the lack of criteria in Assisted Reproductive Technology (ART), we use Machine Learning (ML) techniques to predict oocyte quality for a successful pregnancy. To improve the chances of successful implantation and minimize any complications during the pregnancy, Fourier transform infrared microspectroscopy (FTIRM) analysis has been applied on granulosa cells (GCs) collected along with the oocytes during oocyte aspiration, as it is routinely done in ART, and specific spectral biomarkers were selected by multivariate statistical analysis. A proprietary biological reference dataset (BRD) was successfully collected to predict the best oocyte for a successful pregnancy. Personal health information are stored, maintained and backed up using a cloud computing service. Using a user-friendly interface, the user will evaluate whether or not the selected oocyte will have a positive result. This interface includes a dashboard for retrospective analysis, reporting, real-time processing, and statistical analysis. The experimental results are promising and confirm the efficiency of the method in terms of classification metrics: precision, recall, and F1-score (F1) measures.

EAGLE: Large-Scale Vehicle Detection Dataset in Real-World Scenarios Using Aerial Imagery

Seyed Majid Azimi, Reza Bahmanyar, Corentin Henry, Kurz Franz

Auto-TLDR; EAGLE: A Large-Scale Dataset for Multi-class Vehicle Detection with Object Orientation Information in Airborne Imagery

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Multi-class vehicle detection from airborne imagery with orientation estimation is an important task in the near and remote vision domains with applications in traffic monitoring and disaster management. In the last decade, we have witnessed significant progress in object detection in ground imagery, but it is still in its infancy in airborne imagery, mostly due to the scarcity of diverse and large-scale datasets. Despite being a useful tool for different applications, current airborne datasets only partially reflect the challenges of real-world scenarios. To address this issue, we introduce EAGLE (oriEnted object detection using Aerial imaGery in real-worLd scEnarios), a large-scale dataset for multi-class vehicle detection with object orientation information in aerial imagery. It features high-resolution aerial images composed of different real-world situations with a wide variety of camera sensor, resolution, flight altitude, weather, illumination, haze, shadow, time, city, country, occlusion, and camera angle. The annotation was done by airborne imagery experts with small- and large-vehicle classes. EAGLE contains 215,986 instances annotated with oriented bounding boxes defined by four points and orientation, making it by far the largest dataset to date in this task. It also supports researches on the haze and shadow removal as well as super-resolution and in-painting applications. We define three tasks: detection by (1) horizontal bounding boxes, (2) rotated bounding boxes, and (3) oriented bounding boxes. We carried out several experiments to evaluate several state-of-the-art methods in object detection on our dataset to form a baseline. Experiments show that the EAGLE dataset accurately reflects real-world situations and correspondingly challenging applications. The dataset will be made publicly available.

EDD-Net: An Efficient Defect Detection Network

Tianyu Guo, Linlin Zhang, Runwei Ding, Ge Yang

Auto-TLDR; EfficientNet: Efficient Network for Mobile Phone Surface defect Detection

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As the most commonly used communication tool, the mobile phone has become an indispensable part of our daily life. The surface of the mobile phone as the main window of human-phone interaction directly affects the user experience. It is necessary to detect surface defects on the production line in order to ensure the high quality of the mobile phone. However, the existing mobile phone surface defect detection is mainly done manually, and currently there are few automatic defect detection methods to replace human eyes. How to quickly and accurately detect the surface defects of mobile phone is an urgent problem to be solved. Hence, an efficient defect detection network (EDD-Net) is proposed. Firstly, EfficientNet is used as the backbone network. Then, according to the small-scale of mobile phone surface defects, a feature pyramid module named GCSA-BiFPN is proposed to obtain more discriminative features. Finally, the box/class prediction network is used to achieve effective defect detection. We also build a mobile phone surface oil stain defect (MPSOSD) dataset to alleviate the lack of dataset in this field. The performance on the relevant datasets shows that the network we proposed is effective and has practical significance for industrial production.

Documents Counterfeit Detection through a Deep Learning Approach

Darwin Danilo Saire Pilco, Salvatore Tabbone

Auto-TLDR; End-to-End Learning for Counterfeit Documents Detection using Deep Neural Network

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The main topic of this work is on the detection of counterfeit documents and especially banknotes. We propose an end-to-end learning model using a deep learning approach based on Adapnet++ which manages feature extraction at multiple scale levels using several residual units. Unlike previous models based on regions of interest (ROI) and high-resolution documents, our network is feed with simple input images (i.e., a single patch) and we do not need high resolution images. Besides, discriminative regions can be visualized at different scales. Our network learns by itself which regions of interest predict the better results. Experimental results show that we are competitive compared with the state-of-the-art and our deep neural network has good ability to generalize and can be applied to other kind of documents like identity or administrative one.

Object Features and Face Detection Performance: Analyses with 3D-Rendered Synthetic Data

Jian Han, Sezer Karaoglu, Hoang-An Le, Theo Gevers

Auto-TLDR; Synthetic Data for Face Detection Using 3DU Face Dataset

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This paper is to provide an overview of how object features from images influence face detection performance, and how to select synthetic faces to address specific features. To this end, we investigate the effects of occlusion, scale, viewpoint, background, and noise by using a novel synthetic image generator based on 3DU Face Dataset. To examine the effects of different features, we selected three detectors (Faster RCNN, HR, SSH) as representative of various face detection methodologies. Comparing different configurations of synthetic data on face detection systems, it showed that our synthetic dataset could complement face detectors to become more robust against features in the real world. Our analysis also demonstrated that a variety of data augmentation is necessary to address nuanced differences in performance.

Attention Based Coupled Framework for Road and Pothole Segmentation

Shaik Masihullah, Ritu Garg, Prerana Mukherjee, Anupama Ray

Auto-TLDR; Few Shot Learning for Road and Pothole Segmentation on KITTI and IDD

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In this paper, we propose a novel attention based coupled framework for road and pothole segmentation. In many developing countries as well as in rural areas, the drivable areas are neither well-defined, nor well-maintained. Under such circumstances, an Advance Driver Assistant System (ADAS) is needed to assess the drivable area and alert about the potholes ahead to ensure vehicle safety. Moreover, this information can also be used in structured environments for assessment and maintenance of road health. We demonstrate few shot learning approach for pothole detection to leverage accuracy even with fewer training samples. We report the exhaustive experimental results for road segmentation on KITTI and IDD datasets. We also present pothole segmentation on IDD.

Multi-Scale Deep Pixel Distribution Learning for Concrete Crack Detection

Xuanyi Wu, Jianfei Ma, Yu Sun, Chenqiu Zhao, Anup Basu

Auto-TLDR; Multi-scale Deep Learning for Concrete Crack Detection

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A number of methods including image processing echnologies (IPTs) and deep learning methods have been used to detect defects in civilian infrastructure. These methods have been introduced to extract features representing cracks in concrete surfaces. Inspired by recent advances of a pixel distribution learning method in background subtraction, we propose a novel multi-scale deep learning method (MS-DPDL) for concrete crack detection. The designed CNN network is trained on the dataset CRACK500 [1], [2], and tested on it for concrete segmentation. To show the good transferability of our proposed model, it is later tested on the dataset Concrete Crack Images for crack classification. Several existing deep learning methods are used to compare the performance of the proposed MS-DPDL method. Results show that our method has good performance and can effectively find concrete cracks in practical situations.

Detecting Objects with High Object Region Percentage

Fen Fang, Qianli Xu, Liyuan Li, Ying Gu, Joo-Hwee Lim

Auto-TLDR; Faster R-CNN for High-ORP Object Detection

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Object shape is a subtle but important factor for object detection. It has been observed that the object-region-percentage (ORP) can be utilized to improve detection accuracy for elongated objects, which have much lower ORPs than other types of objects. In this paper, we propose an approach to improve the detection performance for objects whose ORPs are relatively higher.To address the problem of high-ORP object detection, we propose a method consisting of three steps. First, we adjust the ground truth bounding boxes of high-ORP objects to an optimal range. Second, we train an object detector, Faster R-CNN, based on adjusted bounding boxes to achieve high recall. Finally, we train a DCNN to learn the adjustment ratios towards four directions and adjust detected bounding boxes of objects to get better localization for higher precision. We evaluate the effectiveness of our method on 12 high-ORP objects in COCO and 8 objects in a proprietary gearbox dataset. The experimental results show that our method can achieve state-of-the-art performance on these objects while costing less resources in training and inference stages.

Iterative Bounding Box Annotation for Object Detection

Bishwo Adhikari, Heikki Juhani Huttunen

Auto-TLDR; Semi-Automatic Bounding Box Annotation for Object Detection in Digital Images

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Manual annotation of bounding boxes for object detection in digital images is tedious, and time and resource consuming. In this paper, we propose a semi-automatic method for efficient bounding box annotation. The method trains the object detector iteratively on small batches of labeled images and learns to propose bounding boxes for the next batch, after which the human annotator only needs to correct possible errors. We propose an experimental setup for simulating the human actions and use it for comparing different iteration strategies, such as the order in which the data is presented to the annotator. We experiment on our method with three datasets and show that it can reduce the human annotation effort significantly, saving up to 75% of total manual annotation work.

Smart Inference for Multidigit Convolutional Neural Network Based Barcode Decoding

Duy-Thao Do, Tolcha Yalew, Tae Joon Jun, Daeyoung Kim

Auto-TLDR; Smart Inference for Barcode Decoding using Deep Convolutional Neural Network

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Barcodes are ubiquitous and have been used in most of critical daily activities for decades. However, most of traditional decoders require well-founded barcode under a relatively standard condition. While wilder conditioned barcodes such as underexposed, occluded, blurry, wrinkled and rotated are commonly captured in reality, those traditional decoders show weakness of recognizing. Several works attempted to solve those challenging barcodes, but many limitations still exist. This work aims to solve the decoding problem using deep convolutional neural network with the possibility of running on portable devices. Firstly, we proposed a special modification of inference based on the feature of having checksum and test-time augmentation, named as Smart Inference (SI) in prediction phase of a trained model. SI considerably boosts accuracy and reduces the false prediction for trained models. Secondly, we have created a large practical evaluation dataset of real captured 1D barcode under various challenging conditions to test our methods vigorously, which is publicly available for other researchers. The experiments' results demonstrated the SI effectiveness with the highest accuracy of 95.85% which outperformed many existing decoders on the evaluation set. Finally, we successfully minimized the best model by knowledge distillation to a shallow model which is shown to have high accuracy (90.85%) with good inference speed of 34.2 ms per image on a real edge device.

Bridging the Gap between Natural and Medical Images through Deep Colorization

Lia Morra, Luca Piano, Fabrizio Lamberti, Tatiana Tommasi

Auto-TLDR; Transfer Learning for Diagnosis on X-ray Images Using Color Adaptation

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Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and color discrepancy all at once through pretrained model fine-tuning. In this work we propose to disentangle those challenges and design a dedicated network module that focuses on color adaptation. We combine learning from scratch of the color module with transfer learning of different classification backbones obtaining an end-to-end, easy-to-train architecture for diagnostic image recognition on X-ray images. Extensive experiments show how our approach is particularly efficient in case of data scarcity and provides a new path for further transferring the learned color information across multiple medical datasets.

Tracking Fast Moving Objects by Segmentation Network

Ales Zita, Filip Sroubek

Auto-TLDR; Fast Moving Objects Tracking by Segmentation Using Deep Learning

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Tracking Fast Moving Objects (FMO), which appear as blurred streaks in video sequences, is a difficult task for standard trackers, as the object position does not overlap in consecutive video frames and texture information of the objects is blurred. Up-to-date approaches tuned for this task are based on background subtraction with a static background and slow deblurring algorithms. In this article, we present a tracking-by-segmentation approach implemented using modern deep learning methods that perform near real-time tracking on real-world video sequences. We have developed a physically plausible FMO sequence generator to be a robust foundation for our training pipeline and demonstrate straightforward network adaptation for different FMO scenarios with varying foreground.

Relevance Detection in Cataract Surgery Videos by Spatio-Temporal Action Localization

Negin Ghamsarian, Mario Taschwer, Doris Putzgruber, Stephanie. Sarny, Klaus Schoeffmann

Auto-TLDR; relevance-based retrieval in cataract surgery videos

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In cataract surgery, the operation is performed with the help of a microscope. Since the microscope enables watching real-time surgery by up to two people only, a major part of surgical training is conducted using the recorded videos. To optimize the training procedure with the video content, the surgeons require an automatic relevance detection approach. In addition to relevance-based retrieval, these results can be further used for skill assessment and irregularity detection in cataract surgery videos. In this paper, a three-module framework is proposed to detect and classify the relevant phase segments in cataract videos. Taking advantage of an idle frame recognition network, the video is divided into idle and action segments. To boost the performance in relevance detection Mask R-CNN is utilized to detect the cornea in each frame where the relevant surgical actions are conducted. The spatio-temporal localized segments containing higher-resolution information about the pupil texture and actions, and complementary temporal information from the same phase are fed into the relevance detection module. This module consists of four parallel recurrent CNNs being responsible to detect four relevant phases that have been defined with medical experts. The results will then be integrated to classify the action phases as irrelevant or one of four relevant phases. Experimental results reveal that the proposed approach outperforms static CNNs and different configurations of feature-based and end-to-end recurrent networks.

Multi-Attribute Learning with Highly Imbalanced Data

Lady Viviana Beltran Beltran, Mickaël Coustaty, Nicholas Journet, Juan C. Caicedo, Antoine Doucet

Auto-TLDR; Data Imbalance in Multi-Attribute Deep Learning Models: Adaptation to face each one of the problems derived from imbalance

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Data is one of the most important keys for success when studying a simple or a complex phenomenon. With the use of deep-learning exploding and its democratization, non-computer science experts may struggle to use highly complex deep learning architectures, even when straightforward models offer them suitable performances. In this article, we study the specific and common problem of data imbalance in real databases as most of the bad performance problems are due to the data itself. We review two points: first, when the data contains different levels of imbalance. Classical imbalanced learning strategies cannot be directly applied when using multi-attribute deep learning models, i.e., multi-task and multi-label architectures. Therefore, one of our contributions is our proposed adaptations to face each one of the problems derived from imbalance. Second, we demonstrate that with little to no imbalance, straightforward deep learning models work well. However, for non-experts, these models can be seen as black boxes, where all the effort is put in pre-processing the data. To simplify the problem, we performed the classification task ignoring information that is costly to extract, such as part localization which is widely used in the state of the art of attribute classification. We make use of a widely known attribute database, CUB-200-2011 - CUB as our main use case due to its deeply imbalanced nature, along with two better structured databases: celebA and Awa2. All of them contain multi-attribute annotations. The results of highly fine-grained attribute learning over CUB demonstrate that in the presence of imbalance, by using our proposed strategies is possible to have competitive results against the state of the art, while taking advantage of multi-attribute deep learning models. We also report results for two better-structured databases over which our models over-perform the state of the art.

Correlation-Based ConvNet for Small Object Detection in Videos

Brais Bosquet, Manuel Mucientes, Victor Brea

Auto-TLDR; STDnet-ST: An End-to-End Spatio-Temporal Convolutional Neural Network for Small Object Detection in Video

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The detection of small objects is of particular interest in many real applications. In this paper, we propose STDnet-ST, a novel approach to small object detection in video using spatial information operating alongside temporal video information. STDnet-ST is an end-to-end spatio-temporal convolutional neural network that detects small objects over time and correlates pairs of the top-ranked regions with the highest likelihood of containing small objects. This architecture links the small objects across the time as tubelets, being able to dismiss unprofitable object links in order to provide high-quality tubelets. STDnet-ST achieves state-of-the-art results for small objects on the publicly available USC-GRAD-STDdb and UAVDT video datasets.

SyNet: An Ensemble Network for Object Detection in UAV Images

Berat Mert Albaba, Sedat Ozer

Auto-TLDR; SyNet: Combining Multi-Stage and Single-Stage Object Detection for Aerial Images

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Recent advances in camera equipped drone applications and their widespread use increased the demand on vision based object detection algorithms for aerial images. Object detection process is inherently a challenging task as a generic computer vision problem, however, since the use of object detection algorithms on UAVs (or on drones) is relatively a new area, it remains as a more challenging problem to detect objects in aerial images. There are several reasons for that including: (i) the lack of large drone datasets including large object variance, (ii) the large orientation and scale variance in drone images when compared to the ground images, and (iii) the difference in texture and shape features between the ground and the aerial images. Deep learning based object detection algorithms can be classified under two main categories: (a) single-stage detectors and (b) multi-stage detectors. Both single-stage and multi-stage solutions have their advantages and disadvantages over each other. However, a technique to combine the good sides of each of those solutions could yield even a stronger solution than each of those solutions individually. In this paper, we propose an ensemble network, SyNet, that combines a multi-stage method with a single-stage one with the motivation of decreasing the high false negative rate of multi-stage detectors and increasing the quality of the single-stage detector proposals. As building blocks, CenterNet and Cascade R-CNN with pretrained feature extractors are utilized along with an ensembling strategy. We report the state of the art results obtained by our proposed solution on two different datasets: namely MS-COCO and visDrone with \%52.1 $mAP_{IoU = 0.75}$ is obtained on MS-COCO $val2017$ dataset and \%26.2 $mAP_{IoU = 0.75}$ is obtained on VisDrone $test-set$. Our code is available at: https://github.com/mertalbaba/SyNet}{https://github.com/mer talbaba/SyNet