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Dual-Branch Knowledge Distillation via Residual Features Aggregation Module for anomaly segmentation

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhou, You Huang, Zihao Zeng, Deyu Qu, Yanyun Wu, Zongze Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China Tencent Algorithm Applicat Dev Dept Guangzhou 510300 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Peoples R China

Unsupervised image anomaly detection and segmentation algorithms are of great significance in the actual industrial quality inspection process. The anomaly detection method, based on the knowledge distillation framework, detects anomalies by characterizing the differences between abnormal samples. This method uses similar model structures to construct the teacher and student networks, which leads to limited model representation ability and the problem of detection failure due to the disappearance of abnormal activation values. This article solves the problem of disappearing abnormal responses by improving the diversity of model representation. The multiscale input reconstruction branch in the dual-branch knowledge distillation (DBKD) model proposed in this article improves its representation ability by restoring the representation of the input at multiple scales. The multiscale feature information extraction branch enhances its ability to capture defect detail information by extracting feature information at different scales. In addition, we design a residual feature aggregation module (RFAM) to condense the high-dimensional features of the teacher model into compact and effective low-dimensional feature embeddings, which ensures the effectiveness of the multiscale input reconstruction branch input. The proposed DBKD achieves the latest state-of-the-art (SOTA) on the famous MVTec AD dataset, with an area under the receiver operating characteristic curve (ROCAUC) of 98.1% for anomaly detection and 98.2% for anomaly segmentation in all 15 categories.

关键词： Feature extraction Image reconstruction anomaly detection Image segmentation Knowledge engineering Training Generative adversarial networks Defect detection Nearest neighbor methods Mechatronics anomaly segmentation feature embedding knowledge distillation unsupervised learning

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anomaly detection and segmentation in industrial images using multi-scale reverse distillation

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APPLIED SOFT COMPUTING 2025年 168卷

作者： Liu, Chien-Liang Chung, Chia-Chen Natl Yang Ming Chiao Tung Univ Dept Ind Engn & Management 1001 Univ Rd Hsinchu 300 Taiwan

anomaly detection and segmentation in industrial images are critical tasks requiring robust and precise methodologies. This paper presents the Multi-Scale Reverse Distillation (MSRD) methodology, an innovative improvement of the foundational reverse distillation approach. MSRD leverages autoencoder-based techniques integrated with information at different levels to significantly enhance reconstruction capabilities. A novel module incorporated at the decoder's end facilitates precise sample reconstruction. The proposed loss function incorporates the reconstruction loss LRecon, calculated using structural similarity index measure (SSIM) between the original and reconstructed images, in addition to the knowledge distillation loss LKD. Additionally, the integration of a feature pyramid network improves the spatial coherence of anomaly maps across varying scales, enabling detailed anomaly segmentation. The MSRD method undergoes rigorous evaluation on three public datasets, demonstrating superior performance in both anomaly detection and segmentation. The results highlight MSRD's adaptability and effectiveness in one-class learning-based applications. This study underscores MSRD's potential as a powerful tool for industrial anomaly detection, offering significant advancements in AI-driven image analysis.

关键词： anomaly detection anomaly segmentation Autoencoder Information fusion Feature pyramid network Industrial applications

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Unsupervised anomaly segmentation via deep feature reconstruction

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NEUROCOMPUTING 2021年 424卷 9-22页

作者： Shi, Yong Yang, Jie Qi, Zhiquan Univ Chinese Acad Sci Sch Econ & Management Beijing 101408 Peoples R China Univ Chinese Acad Sci Sch Comp & Technol Beijing 101408 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Automatic detecting anomalous regions in images of objects or textures without priors of the anomalies is challenging, especially when the anomalies appear in very small areas of the images, making difficult to-detect visual variations, such as defects on manufacturing products. This paper proposes an effective unsupervised anomaly segmentation approach that can detect and segment out the anomalies in small and confined regions of images. Concretely, we develop a multi-scale regional feature generator which can generate multiple spatial context-aware representations from pre-trained deep convolutional networks for every subregion of an image. The regional representations not only describe the local characteristics of corresponding regions but also encode their multiple spatial context information, making them discriminative and very beneficial for anomaly detection. Leveraging these descriptive regional features, we then design a deep yet efficient convolutional autoencoder and detect anomalous regions within images via fast feature reconstruction. Our method is simple yet effective and efficient. It advances the state-of-the-art performances on several benchmark datasets and shows great potential for real applications. (c) 2020 Published by Elsevier B.V.

关键词： anomaly detection anomaly segmentation Regional representation Feature reconstruction

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Unsupervised intrusion detection for rail transit based on anomaly segmentation

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第2期18卷 1079-1087页

作者： Shen, Yixin He, Deqiang Liu, Qi Jin, Zhenzhen Li, Xianwang Ren, Chonghui Guangxi Univ Sch Mech Engn Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530004 Guangxi Peoples R China Nanning Rail Transit Co Ltd Nanning 530029 Guangxi Peoples R China

Detecting intrusions in rail transit can be challenging using traditional supervised methods, as they only detect target categories present in the training dataset and require extensive manual annotations. This paper proposes an unsupervised method for railroad intrusion detection based on anomaly segmentation, called heterogeneous uninformed students network (HUS-Net). No obstacle data is needed for training with this method, and it does not restrict identified objects to specific categories. HUS-Net utilizes a pre-trained descriptive model as the teacher network and distils its knowledge into two heterogeneous students via multi-level feature pyramid matching and reconstruction techniques. The representation discrepancy between the students and the teacher is utilized to identify railroad intrusion events and locate anomalous objects. The model is evaluated on images captured by an onboard vision system in real rail transit operating environments. Experimental results demonstrate that HUS-Net can accurately and efficiently detect intrusion events on railroads and segment invading objects, achieving better performance than other anomaly segmentation methods.

关键词： Rail transit anomaly segmentation Knowledge distillation Intrusion detection

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Deep Feature Contrasting for Industrial Image anomaly segmentation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Wan, Qian Cao, Yunkang Gao, Liang Li, Xinyu Gao, Yiping Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Industrial image anomaly segmentation is pivotal in ensuring the quality inspection of products within intelligent manufacturing systems. Recent research efforts have predominantly focused on deep learning-based approaches to address this challenge. However, unsupervised methods are often susceptible to distribution shifting, while supervised methods face significant obstacles due to imbalanced samples, resulting in suboptimal accuracy for anomaly segmentation. This article introduces a novel end-to-end deep feature contrasting (DFC) method aimed at enhancing segmentation accuracy in scenarios with limited supervision and pixel-level labeled anomalous images. DFC includes the introduction of a novel backbone feature contrasting pyramid (BFCP) featuring dual-model channels, which effectively captures the distribution of normality. In addition, a novel model-independent semantic feature contrasting (SFC) technique, implemented through self-contrasting, is proposed to train a discriminative segmenting head module (SHM) that addresses the challenge of sample unbalance. Furthermore, this article presents two types of SFC, namely, global and local, which are suggested to improve the overall performance. The proposed DFC method achieves F1 of 0.787 and 0.665 on two real-world industrial datasets (Kolektor Surface Defect Dataset (KSDD) and KSDD2) and is applied to a practical application of anomaly segmentation of printed circuit boards, demonstrating the superior anomaly segmenting performance.

关键词： Feature extraction Semantics Training Semantic segmentation Testing Convolutional neural networks Neural networks anomaly segmentation deep feature contrasting (DFC) image anomaly

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Test Time Training for Industrial anomaly segmentation

Test Time Training for Industrial Anomaly Segmentation

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Costanzino, Alex Ramirez, Pierluigi Zama Del Moro, Mirko Aiezzo, Agostino Lisanti, Giuseppe Salti, Samuele Di Stefano, Luigi Univ Bologna CVLAB Dept Comp Sci & Engn DISI Bologna Italy

ISBN: (纸本)9798350365474

anomaly Detection and segmentation (AD&S) is crucial for industrial quality control. While existing methods excel in generating anomaly scores for each pixel, practical applications require producing a binary segmentation to identify anomalies. Due to the absence of labeled anomalies in many real scenarios, standard practices binarize these maps based on some statistics derived from a validation set containing only nominal samples, resulting in poor segmentation performance. This paper addresses this problem by proposing a test time training strategy to improve the segmentation performance. Indeed, at test time, we can extract rich features directly from anomalous samples to train a classifier that can discriminate defects effectively. Our general approach can work downstream to any AD&S method that provides an anomaly score map as output, even in multimodal settings. We demonstrate the effectiveness of our approach over baselines through extensive experimentation and evaluation on MVTec AD and MVTec 3D-AD.

关键词： anomaly anomaly detection anomaly localization anomaly scores anomaly segmentation binary segmentation mvtec mvtec3dad mvtecad pseudolabels test time adaptation test time training

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Predictable Uncertainty-Aware Unsupervised Deep anomaly segmentation

Predictable Uncertainty-Aware Unsupervised Deep Anomaly Segm...

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International Joint Conference on Neural Networks (IJCNN)

作者： Sato, Kazuki Hama, Kenta Matsubara, Takashi Uehara, Kuniaki Kobe Univ Grad Sch Syst Informat Nada Ku 1-1 Rokkodai Kobe Hyogo 6578501 Japan

ISBN: (纸本)9781728119854

Image-based anomaly segmentation is a fundamental topic for image analysis. For medical use, it supports treatments via refined diagnosis and growth rate evaluation of tumors and lesions. Especially, an unsupervised training is expected to generalize to unknown anomalies. Probabilistic models have been used for this purpose, whereby these models are trained to maximize the likelihood of known samples and detect anomalous samples by assigning low likelihoods. Recent studies have proposed a probabilistic model based on deep neural networks (DNNs) called AEs and they achieved significant performance thanks to their flexibility. However, AEs are sensitive to complex structure (e.g., ridges and grooves of a brain) rather than semantic anomalies (e.g., tumors and lesions). We decomposed the approximated log-likelihood into two terms;predictable uncertainty and normalized error. We found that the former represents the complexity of structure. Hence, we propose the normalized error as a novel uncertainty-sensitive score by removing the predictable uncertainty. We evaluated our score by experiments with head magnetic resonance imaging (MRI) datasets and demonstrate the robustness of the proposed normalized error to data complexity.

关键词： deep learning anomaly segmentation unsupervised learning uncertainty

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Improving anomaly segmentation with Multi-Granularity Cross-Domain Alignment 23

Improving Anomaly Segmentation with Multi-Granularity Cross-...

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31st ACM International Conference on Multimedia (MM)

作者： Zhang, Ji Wu, Xiao Cheng, Zhi-Qi He, Qi Li, Wei Southwest Jiaotong Univ Chengdu Peoples R China Minist Educ China Engn Res Ctr Sustainable Urban Intelligent Transp Chengdu Peoples R China Carnegie Mellon Univ Sch Comp Sci Language Technol Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9798400701085

anomaly segmentation plays a crucial role in identifying anomalous objects within images, which facilitates the detection of road anomalies for autonomous driving. Although existing methods have shown impressive results in anomaly segmentation using synthetic training data, the domain discrepancies between synthetic training data and real test data are often neglected. To address this issue, the Multi-Granularity Cross-Domain Alignment (MGCDA) framework is proposed for anomaly segmentation in complex driving environments. It uniquely combines a new Multi-source Domain Adversarial Training (MDAT) module and a novel Cross-domain anomaly-aware Contrastive Learning (CACL) method to boost the generality of the model, seamlessly integrating multi-domain data at both scene and sample levels. Multi-source domain adversarial loss and a dynamic label smoothing strategy are integrated into the MDAT module to facilitate the acquisition of domain-invariant features at the scene level, through adversarial training across multiple stages. CACL aligns sample-level representations with contrastive loss on cross-domain data, which utilizes an anomaly-aware sampling strategy to efficiently sample hard samples and anchors. The proposed framework has decent properties of parameter-free during the inference stage and is compatible with other anomaly segmentation networks. Experimental conducted on Fishyscapes and Roadanomaly datasets demonstrate that the proposed framework achieves state-of-the-art performance.

关键词： anomaly segmentation Domain adversarial training

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Transformer Based Models for Unsupervised anomaly segmentation in Brain MR Images 8th

Transformer Based Models for Unsupervised Anomaly Segmentati...

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8th International Workshop on Brain Lesion - Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes)

作者： Ghorbel, Ahmed Aldahdooh, Ahmed Albarqouni, Shadi Hamidouche, Wassim Univ Rennes INSA Rennes CNRS IETR UMR 6164 20 Av Buttes Coesmes F-35700 Rennes France Univ Hosp Bonn Venusberg Campus 1 D-53127 Bonn Germany Helmholtz Munich Ingolstadter Landstr 1 D-85764 Neuherberg Germany Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany

ISBN: (纸本)9783031338410;9783031338427

The quality of patient care associated with diagnostic radiology is proportionate to a physician's workload. segmentation is a fundamental limiting precursor to both diagnostic and therapeutic procedures. Advances in machine learning (ML) aim to increase diagnostic efficiency by replacing a single application with generalized algorithms. The goal of unsupervised anomaly detection (UAD) is to identify potential anomalous regions unseen during training, where convolutional neural network (CNN) based autoencoders (AEs), and variational autoencoders (VAEs) are considered a de facto approach for reconstruction based-anomaly segmentation. The restricted receptive field in CNNs limits the CNN to model the global context. Hence, if the anomalous regions cover large parts of the image, the CNN-based AEs are not capable of bringing a semantic understanding of the image. Meanwhile, vision transformers (ViTs) have emerged as a competitive alternative to CNNs. It relies on the self-attention mechanism that can relate image patches to each other. We investigate in this paper Transformer's capabilities in building AEs for the reconstruction-based UAD task to reconstruct a coherent and more realistic image. We focus on anomaly segmentation for brain magnetic resonance imaging (MRI) and present five Transformer-based models while enabling segmentation performance comparable to or superior to state-of-the-art (SOTA) models. The source code is made publicly available on GitHub.

关键词： Unsupervised Learning anomaly segmentation Deep-Learning Transformers Neuroimaging

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The anomaly segmentation via dynamic branch fusion

The anomaly segmentation via dynamic branch fusion

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9th IEEE International Conference on Big Data (IEEE BigData)

作者： Liu, Zihang Zhou, Ejian Yang, Shuwen Zhuang, Zisong Ma, Tianlong East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781665439022

anomaly segmentation is an important task in computer vision. At present, anomaly segmentation has achieved milestone development. Many representative works have been proposed, especially unsupervised learning methods and pre-training methods, but pre-training methods are difficult to solve cross- The gap brought by domains has not been paid much attention to in previous research work on how to mine the hidden information contained in the data itself. Because of the diversity of anomaly detection, it is crucial to make full use of the information of the object itself. This paper proposes a novel dynamic branch fusion structure, through mining the hidden information inside the data, so as to re-model more targeted abnormal segmentation. The method has been tested on two known benchmarks to verify the effectiveness of the proposed method.

关键词： anomaly segmentation computer vision unsupervised learning

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