Automatic traffic sign detection and recognition has achieved good results using convolutional ne... more Automatic traffic sign detection and recognition has achieved good results using convolutional neural networks. Novel architectures are still being proposed in order to improve accuracy of detection and segmentation of traffic sings. In this paper, we are examining the possibility for traffic sign detection and recognition in real-time. For that purpose, we employed a novel YOLOv3 architecture, which has been proven to be fast and accurate method for object detection. It was shown that real-time detection can be achieved, even on HD images, with mAP above 88%.
Anomaly detection in complex data structures is one of the most challenging problems in computer ... more Anomaly detection in complex data structures is one of the most challenging problems in computer vision. In many real-world problems, for example in the quality control in modern manufacturing, the anomalous samples are usually rare, resulting in (highly) imbalanced datasets. However, in current research practice, these scenarios are rarely modeled, and as a consequence, evaluation of anomaly detection algorithms often do not reproduce results that are useful for practical applications. First, even in case of highly unbalanced input data, anomaly detection algorithms are expected to significantly reduce the proportion of anomalous samples, detecting “almost all” anomalous samples (with exact specifications depending on the target customer). This places high importance on only the small part of the ROC curve, possibly rendering the standard metrics such as AUC (Area Under Curve) and AP (Average Precision) useless. Second, the target of automatic anomaly detection in practical applications is significant reduction in manual work required, and standard metrics are poor predictor of this feature. Finally, the evaluation may produce erratic results for different randomly initialized training runs of the neural network, producing evaluation results that may not reproduce well in practice. In this paper, we present an evaluation methodology that avoids these pitfalls.
Recent trends in the development of autonomous vehicles focus on real-time processing of vast amo... more Recent trends in the development of autonomous vehicles focus on real-time processing of vast amounts of data from various sensors. The data can be acquired using multiple cameras, lidars, ultrasonic sensors, and radars to collect useful information about the state of the traffic and the surroundings. Significant computational power is required to process the data fast enough, and this is even more pronounced in vehicles that not only assist the driver but are capable of fully autonomous driving. This article proposes speed and accuracy improvement of traffic sign detection and recognition in high-definition images, based on focusing on different regions of interest in traffic images. These regions are determined with efficient and parallelized preprocessing of every traffic image, after which convolutional neural network is applied for detection and recognition in parallel on graphics processing units. We employed different ''You Only Look Once'' (YOLO) architectures as baseline detectors, due to their speed, straightforward architecture, and high accuracy in general object detection tasks. Several preprocessing procedures were proposed, to achieve real-time performance requirement. Our experiments using a large-scale traffic sign dataset show that we can achieve real-time detection in high-definition images with high recognition accuracy.
Visual anomaly detection addresses the problem of classification or localization of regions in an... more Visual anomaly detection addresses the problem of classification or localization of regions in an image that deviate from their normal appearance. A popular approach trains an auto-encoder on anomaly-free images and performs anomaly detection by calculating the difference between the input and the reconstructed image. This approach assumes that the auto-encoder will be unable to accurately reconstruct anomalous regions. But in practice neural networks generalize well even to anomalies and reconstruct them sufficiently well, thus reducing the detection capabilities. Accurate reconstruction is far less likely if the anomaly pixels were not visible to the auto-encoder. We thus cast anomaly detection as a self-supervised reconstruction-by-inpainting problem. Our approach (RIAD) randomly removes partial image regions and reconstructs the image from partial inpaintings, thus addressing the drawbacks of auto-enocoding methods. RIAD is extensively evaluated on several benchmarks and sets a new state-of-the art on a recent highly challenging anomaly detection benchmark.
Ena večjih preprek uporabe globokega učenja v industriji je zahteva po velikem številu označenih ... more Ena večjih preprek uporabe globokega učenja v industriji je zahteva po velikem številu označenih slik potrebnih za nadzorovano učenje. Ker je pridobivanje označenih slik pogosto zamuden in drag proces, se veliko metod skuša izogniti temu problemu z bogatenjem učnih slik, ki so na voljo, ali z uporabo sintetičnih slik za predhodno učenje in poznejše doučenje z resničnimi slikami. V tem članku predlagamo metodo za generiranje sintetičnih slik iz danih 3D CAD modelov in novo podatkovno množico sestavljeno iz sintetičnih in resničnih slik ter njihovih segmentacijskih slik. Obravnavamo torej scenarij učenja segmentacijskega modela zgolj iz sintetičnih slik, ki so v industriji tipično na voljo, in zagotavlja najhitrejšo prilagoditev proizvodnega procesa na nove izdelke brez potrebe po zajemanju pravih učnih slik. Na predlagani podatkovni množici ovrednotimo algoritem za segmentacijo slik in primerjamo rezultate dobljene z različnim številom sintetičnih in realnih slik industrijskega predmeta posnetega oz. upodobljenega na različnih ozadjih.
Visual surface anomaly detection aims to detect local image regions that significantly deviate fr... more Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies. These methods are trained only on anomaly-free images, and often require hand-crafted post-processing steps to localize the anomalies, which prohibits optimizing the feature extraction for maximal detection capability. In addition to reconstructive approach, we cast surface anomaly detection primarily as a discriminative problem and propose a discriminatively trained reconstruction anomaly embedding model (DRAEM). The proposed method learns a joint representation of an anomalous image and its anomaly-free reconstruction, while simultaneously learning a decision boundary between normal and anomalous examples. The method enables direct anomaly localization without the need for additional complicated post-processing of the network output and can be trained using simple and general anomaly simulations. On the challenging MVTec anomaly detection dataset, DRAEM outperforms the current state-of-theart unsupervised methods by a large margin and even delivers detection performance close to the fully-supervised methods on the widely used DAGM surface-defect detection dataset, while substantially outperforming them in localization accuracy.
Segmentation-based, two-stage neural network has shown excellent results in the surface defect de... more 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 the results on the surface defect detection tasks. To enable end-toend 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 an 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.
In this paper we present a ship detection pipeline for low-cost medium resolution satellite optic... more In this paper we present a ship detection pipeline for low-cost medium resolution satellite optical imagery obtained from ESA Sentinel-2 and Planet Labs Dove constellations. This optical satellite imagery is readily available for any place on Earth and underutilized in maritime domain, compared to existing solutions based on synthetic-aperture radar (SAR) imagery. We developed ship detection method based on state-of-the-art deeplearning based object detection method which was developed and evaluated on a large scale dataset that was collected and automatically annotated with the help of Automatic Identification System (AIS) data.
International Journal of Advanced Robotic Systems, 2021
Mobile robots that operate in real-world environments need to be able to safely navigate their su... more Mobile robots that operate in real-world environments need to be able to safely navigate their surroundings. Obstacle avoidance and path planning are crucial capabilities for achieving autonomy of such systems. However, for new or dynamic environments, navigation methods that rely on an explicit map of the environment can be impractical or even impossible to use. We present a new local navigation method for steering the robot to global goals without relying on an explicit map of the environment. The proposed navigation model is trained in a deep reinforcement learning framework based on Advantage Actor–Critic method and is able to directly translate robot observations to movement commands. We evaluate and compare the proposed navigation method with standard map-based approaches on several navigation scenarios in simulation and demonstrate that our method is able to navigate the robot also without the map or when the map gets corrupted, while the standard approaches fail. We also show that our method can be directly transferred to a real robot.
Deep-learning methods have recently started being employed for addressing surface-defect detectio... more Deep-learning methods have recently started being employed for addressing surface-defect detection problems in industrial quality control. However, with a large amount of data needed for learning, often requiring high-precision labels, many industrial problems cannot be easily solved, or the cost of the solutions would significantly increase due to the annotation requirements. In this work, we relax heavy requirements of fully supervised learning methods and reduce the need for highly detailed annotations. By proposing a deep-learning architecture, we explore the use of annotations of different details ranging from weak (image-level) labels through mixed supervision to full (pixel-level) annotations on the task of surface-defect detection. The proposed end-to-end architecture is composed of two sub-networks yielding defect segmentation and classification results. The proposed method is evaluated on several datasets for industrial quality inspection: KolektorSDD, DAGM and Severstal Steel Defect. We also present a new dataset termed KolektorSDD2 with over 3000 images containing several types of defects, obtained while addressing a real-world industrial problem. We demonstrate state-of-the-art results on all four datasets. The proposed method outperforms all related approaches in fully supervised settings and also outperforms weakly-supervised methods when only imagelevel labels are available. We also show that mixed supervision with only a handful of fully annotated samples added to weakly labelled training images can result in performance comparable to the fully supervised model's performance but at a significantly lower annotation cost.
Local navigation is an essential ability of any mobile robot working in a real-world environment.... more Local navigation is an essential ability of any mobile robot working in a real-world environment. One of the most commonly used methods for local navigation is the Dynamic Window Approach (DWA), which heavily depends on the settings of the parameters in its cost function. Since the optimal choice of the parameters depends on the environment that may significantly vary and change at any time, the parameters should be chosen dynamically in a data-driven way. To cope with this problem, we propose a novel deep convolutional neural network, which dynamically predicts these parameters considering the sensor readings. The network is trained using a state-of-the art reinforcement learning algorithm. In this way, we combine the power of data-driven learning and the dynamic model of the robot, enabling adaptation to the current environment as well as guaranteeing collision-free movement and smooth trajectories of the mobile robot. The experimental results show that the proposed method outperforms the DWA method as well as its recent extension.
Overly simplified evaluation of adversarial attacks Focus is only on 100% error rates. Even 50% e... more Overly simplified evaluation of adversarial attacks Focus is only on 100% error rates. Even 50% error rate is problematic in the real world.
Automating optical-inspection systems using machine learning has become an interesting and promis... more Automating optical-inspection systems using machine learning has become an interesting and promising area of research. In particular, the deeplearning approaches have shown a very high and direct impact on the application domain of visual inspection. This paper presents a complete inspection system for automated quality control of a specific industrial product. Both hardware and software part of the system are described, with machine vision used for image acquisition and pre-processing followed by a segmentation-based deep-learning model used for surface-defect detection. The deep-learning model is compared with the state-of-the-art commercial software, showing that the proposed approach outperforms the related method on the specific domain of surface-crack detection. Experiments are performed on a real-world quality-control case and demonstrate that the deep-learning model can be successfully used even when only 33 defective training samples are available. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited.
Advances in intelligent systems and computing, Sep 2, 2018
In this paper we present and evaluate the usage of an open-source robotic manipulator platform, t... more In this paper we present and evaluate the usage of an open-source robotic manipulator platform, that we have developed, in the context of various educational scenarios that we have conducted. The system was tested in multiple diverse learning scenarios, ranging from a summer school for primary-school students, to the course at the university level study. We show that the introduction of the system in the educational process improves the motivation as well as acquired knowledge of the participants.
Convolutional neural methods have proven to outperform other approaches in various computer visio... more Convolutional neural methods have proven to outperform other approaches in various computer vision tasks. In this paper we apply the deep learning technique to the domain of automated visual surface inspection. We design a unified CNN-based framework for segmentation and detection of surface anomalies. We investigate whether a compact CNN architecture, which exhibit fewer parameters that need to be learned, can be used, while retaining high classification accuracy. We propose and evaluate a compact CNN architecture on a dataset consisting of diverse textured surfaces with variously-shaped weakly-labeled anomalies. The proposed approach achieves state-of-the-art results in terms of anomaly segmentation as well as classification.
Automatic traffic sign detection and recognition has achieved good results using convolutional ne... more Automatic traffic sign detection and recognition has achieved good results using convolutional neural networks. Novel architectures are still being proposed in order to improve accuracy of detection and segmentation of traffic sings. In this paper, we are examining the possibility for traffic sign detection and recognition in real-time. For that purpose, we employed a novel YOLOv3 architecture, which has been proven to be fast and accurate method for object detection. It was shown that real-time detection can be achieved, even on HD images, with mAP above 88%.
Anomaly detection in complex data structures is one of the most challenging problems in computer ... more Anomaly detection in complex data structures is one of the most challenging problems in computer vision. In many real-world problems, for example in the quality control in modern manufacturing, the anomalous samples are usually rare, resulting in (highly) imbalanced datasets. However, in current research practice, these scenarios are rarely modeled, and as a consequence, evaluation of anomaly detection algorithms often do not reproduce results that are useful for practical applications. First, even in case of highly unbalanced input data, anomaly detection algorithms are expected to significantly reduce the proportion of anomalous samples, detecting “almost all” anomalous samples (with exact specifications depending on the target customer). This places high importance on only the small part of the ROC curve, possibly rendering the standard metrics such as AUC (Area Under Curve) and AP (Average Precision) useless. Second, the target of automatic anomaly detection in practical applications is significant reduction in manual work required, and standard metrics are poor predictor of this feature. Finally, the evaluation may produce erratic results for different randomly initialized training runs of the neural network, producing evaluation results that may not reproduce well in practice. In this paper, we present an evaluation methodology that avoids these pitfalls.
Recent trends in the development of autonomous vehicles focus on real-time processing of vast amo... more Recent trends in the development of autonomous vehicles focus on real-time processing of vast amounts of data from various sensors. The data can be acquired using multiple cameras, lidars, ultrasonic sensors, and radars to collect useful information about the state of the traffic and the surroundings. Significant computational power is required to process the data fast enough, and this is even more pronounced in vehicles that not only assist the driver but are capable of fully autonomous driving. This article proposes speed and accuracy improvement of traffic sign detection and recognition in high-definition images, based on focusing on different regions of interest in traffic images. These regions are determined with efficient and parallelized preprocessing of every traffic image, after which convolutional neural network is applied for detection and recognition in parallel on graphics processing units. We employed different ''You Only Look Once'' (YOLO) architectures as baseline detectors, due to their speed, straightforward architecture, and high accuracy in general object detection tasks. Several preprocessing procedures were proposed, to achieve real-time performance requirement. Our experiments using a large-scale traffic sign dataset show that we can achieve real-time detection in high-definition images with high recognition accuracy.
Visual anomaly detection addresses the problem of classification or localization of regions in an... more Visual anomaly detection addresses the problem of classification or localization of regions in an image that deviate from their normal appearance. A popular approach trains an auto-encoder on anomaly-free images and performs anomaly detection by calculating the difference between the input and the reconstructed image. This approach assumes that the auto-encoder will be unable to accurately reconstruct anomalous regions. But in practice neural networks generalize well even to anomalies and reconstruct them sufficiently well, thus reducing the detection capabilities. Accurate reconstruction is far less likely if the anomaly pixels were not visible to the auto-encoder. We thus cast anomaly detection as a self-supervised reconstruction-by-inpainting problem. Our approach (RIAD) randomly removes partial image regions and reconstructs the image from partial inpaintings, thus addressing the drawbacks of auto-enocoding methods. RIAD is extensively evaluated on several benchmarks and sets a new state-of-the art on a recent highly challenging anomaly detection benchmark.
Ena večjih preprek uporabe globokega učenja v industriji je zahteva po velikem številu označenih ... more Ena večjih preprek uporabe globokega učenja v industriji je zahteva po velikem številu označenih slik potrebnih za nadzorovano učenje. Ker je pridobivanje označenih slik pogosto zamuden in drag proces, se veliko metod skuša izogniti temu problemu z bogatenjem učnih slik, ki so na voljo, ali z uporabo sintetičnih slik za predhodno učenje in poznejše doučenje z resničnimi slikami. V tem članku predlagamo metodo za generiranje sintetičnih slik iz danih 3D CAD modelov in novo podatkovno množico sestavljeno iz sintetičnih in resničnih slik ter njihovih segmentacijskih slik. Obravnavamo torej scenarij učenja segmentacijskega modela zgolj iz sintetičnih slik, ki so v industriji tipično na voljo, in zagotavlja najhitrejšo prilagoditev proizvodnega procesa na nove izdelke brez potrebe po zajemanju pravih učnih slik. Na predlagani podatkovni množici ovrednotimo algoritem za segmentacijo slik in primerjamo rezultate dobljene z različnim številom sintetičnih in realnih slik industrijskega predmeta posnetega oz. upodobljenega na različnih ozadjih.
Visual surface anomaly detection aims to detect local image regions that significantly deviate fr... more Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies. These methods are trained only on anomaly-free images, and often require hand-crafted post-processing steps to localize the anomalies, which prohibits optimizing the feature extraction for maximal detection capability. In addition to reconstructive approach, we cast surface anomaly detection primarily as a discriminative problem and propose a discriminatively trained reconstruction anomaly embedding model (DRAEM). The proposed method learns a joint representation of an anomalous image and its anomaly-free reconstruction, while simultaneously learning a decision boundary between normal and anomalous examples. The method enables direct anomaly localization without the need for additional complicated post-processing of the network output and can be trained using simple and general anomaly simulations. On the challenging MVTec anomaly detection dataset, DRAEM outperforms the current state-of-theart unsupervised methods by a large margin and even delivers detection performance close to the fully-supervised methods on the widely used DAGM surface-defect detection dataset, while substantially outperforming them in localization accuracy.
Segmentation-based, two-stage neural network has shown excellent results in the surface defect de... more 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 the results on the surface defect detection tasks. To enable end-toend 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 an 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.
In this paper we present a ship detection pipeline for low-cost medium resolution satellite optic... more In this paper we present a ship detection pipeline for low-cost medium resolution satellite optical imagery obtained from ESA Sentinel-2 and Planet Labs Dove constellations. This optical satellite imagery is readily available for any place on Earth and underutilized in maritime domain, compared to existing solutions based on synthetic-aperture radar (SAR) imagery. We developed ship detection method based on state-of-the-art deeplearning based object detection method which was developed and evaluated on a large scale dataset that was collected and automatically annotated with the help of Automatic Identification System (AIS) data.
International Journal of Advanced Robotic Systems, 2021
Mobile robots that operate in real-world environments need to be able to safely navigate their su... more Mobile robots that operate in real-world environments need to be able to safely navigate their surroundings. Obstacle avoidance and path planning are crucial capabilities for achieving autonomy of such systems. However, for new or dynamic environments, navigation methods that rely on an explicit map of the environment can be impractical or even impossible to use. We present a new local navigation method for steering the robot to global goals without relying on an explicit map of the environment. The proposed navigation model is trained in a deep reinforcement learning framework based on Advantage Actor–Critic method and is able to directly translate robot observations to movement commands. We evaluate and compare the proposed navigation method with standard map-based approaches on several navigation scenarios in simulation and demonstrate that our method is able to navigate the robot also without the map or when the map gets corrupted, while the standard approaches fail. We also show that our method can be directly transferred to a real robot.
Deep-learning methods have recently started being employed for addressing surface-defect detectio... more Deep-learning methods have recently started being employed for addressing surface-defect detection problems in industrial quality control. However, with a large amount of data needed for learning, often requiring high-precision labels, many industrial problems cannot be easily solved, or the cost of the solutions would significantly increase due to the annotation requirements. In this work, we relax heavy requirements of fully supervised learning methods and reduce the need for highly detailed annotations. By proposing a deep-learning architecture, we explore the use of annotations of different details ranging from weak (image-level) labels through mixed supervision to full (pixel-level) annotations on the task of surface-defect detection. The proposed end-to-end architecture is composed of two sub-networks yielding defect segmentation and classification results. The proposed method is evaluated on several datasets for industrial quality inspection: KolektorSDD, DAGM and Severstal Steel Defect. We also present a new dataset termed KolektorSDD2 with over 3000 images containing several types of defects, obtained while addressing a real-world industrial problem. We demonstrate state-of-the-art results on all four datasets. The proposed method outperforms all related approaches in fully supervised settings and also outperforms weakly-supervised methods when only imagelevel labels are available. We also show that mixed supervision with only a handful of fully annotated samples added to weakly labelled training images can result in performance comparable to the fully supervised model's performance but at a significantly lower annotation cost.
Local navigation is an essential ability of any mobile robot working in a real-world environment.... more Local navigation is an essential ability of any mobile robot working in a real-world environment. One of the most commonly used methods for local navigation is the Dynamic Window Approach (DWA), which heavily depends on the settings of the parameters in its cost function. Since the optimal choice of the parameters depends on the environment that may significantly vary and change at any time, the parameters should be chosen dynamically in a data-driven way. To cope with this problem, we propose a novel deep convolutional neural network, which dynamically predicts these parameters considering the sensor readings. The network is trained using a state-of-the art reinforcement learning algorithm. In this way, we combine the power of data-driven learning and the dynamic model of the robot, enabling adaptation to the current environment as well as guaranteeing collision-free movement and smooth trajectories of the mobile robot. The experimental results show that the proposed method outperforms the DWA method as well as its recent extension.
Overly simplified evaluation of adversarial attacks Focus is only on 100% error rates. Even 50% e... more Overly simplified evaluation of adversarial attacks Focus is only on 100% error rates. Even 50% error rate is problematic in the real world.
Automating optical-inspection systems using machine learning has become an interesting and promis... more Automating optical-inspection systems using machine learning has become an interesting and promising area of research. In particular, the deeplearning approaches have shown a very high and direct impact on the application domain of visual inspection. This paper presents a complete inspection system for automated quality control of a specific industrial product. Both hardware and software part of the system are described, with machine vision used for image acquisition and pre-processing followed by a segmentation-based deep-learning model used for surface-defect detection. The deep-learning model is compared with the state-of-the-art commercial software, showing that the proposed approach outperforms the related method on the specific domain of surface-crack detection. Experiments are performed on a real-world quality-control case and demonstrate that the deep-learning model can be successfully used even when only 33 defective training samples are available. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited.
Advances in intelligent systems and computing, Sep 2, 2018
In this paper we present and evaluate the usage of an open-source robotic manipulator platform, t... more In this paper we present and evaluate the usage of an open-source robotic manipulator platform, that we have developed, in the context of various educational scenarios that we have conducted. The system was tested in multiple diverse learning scenarios, ranging from a summer school for primary-school students, to the course at the university level study. We show that the introduction of the system in the educational process improves the motivation as well as acquired knowledge of the participants.
Convolutional neural methods have proven to outperform other approaches in various computer visio... more Convolutional neural methods have proven to outperform other approaches in various computer vision tasks. In this paper we apply the deep learning technique to the domain of automated visual surface inspection. We design a unified CNN-based framework for segmentation and detection of surface anomalies. We investigate whether a compact CNN architecture, which exhibit fewer parameters that need to be learned, can be used, while retaining high classification accuracy. We propose and evaluate a compact CNN architecture on a dataset consisting of diverse textured surfaces with variously-shaped weakly-labeled anomalies. The proposed approach achieves state-of-the-art results in terms of anomaly segmentation as well as classification.
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Papers by Danijel Skočaj