A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method

Despite significant advancements in self-supervised anomaly detection, multi-class anomaly detection tasks still pose substantial challenges. Most existing methods require individual network training for each category of objects. This paper presents a novel end-to-end approach for multi-class anomal...
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Autor*in:	Yibo Chen [verfasserIn] Haolong Peng [verfasserIn] Le Huang [verfasserIn] Jianming Zhang [verfasserIn] Wei Jiang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Defect localization self-supervised learning visual transformer (ViT) masked autoencoder (MAE) industrial products

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 11(2023), Seite 127526-127538
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:127526-127538

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2023.3332475

Katalog-ID:	DOAJ09268470X

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520			\|a Despite significant advancements in self-supervised anomaly detection, multi-class anomaly detection tasks still pose substantial challenges. Most existing methods require individual network training for each category of objects. This paper presents a novel end-to-end approach for multi-class anomaly detection: self-supervised Mask-pretrained Anomaly Localization Autoencoder (MALA). Firstly, the masked autoencoder (MAE) and Pseudo Label Prediction Module (PLPM) are utilized to recover and perceive normal image patterns. Subsequently, the encoder weights are frozen for further end-to-end network training to predict anomalous maps directly. Token Balance Module(TBM) facilitates anomalous perception and improves anomaly segmentation. By utilizing the Visual Transformer and employing image inpainting as a proxy task, remarkable generalization results are achieved. The proposed method demonstrates its applicability across diverse styles of industrial products. Experiments are conducted on MVTech AD, VisA, KolektorSDD2, and MT datasets, achieving state-of-the-art results in multi-task anomaly detection and segmentation tasks. Specifically, we obtain image AUROC of 98.% and pixel AUROC of 97.1% on the MVTech AD dataset, pixel AUROC of 97.1% on the VisA dataset, and pixel AUROC of 98.7% on the KolektorSDD2 dataset.
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allfieldsGer	10.1109/ACCESS.2023.3332475 doi (DE-627)DOAJ09268470X (DE-599)DOAJ18b6318a0a3d44ec854e8808a830b8a1 DE-627 ger DE-627 rakwb eng TK1-9971 Yibo Chen verfasserin aut A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Despite significant advancements in self-supervised anomaly detection, multi-class anomaly detection tasks still pose substantial challenges. Most existing methods require individual network training for each category of objects. This paper presents a novel end-to-end approach for multi-class anomaly detection: self-supervised Mask-pretrained Anomaly Localization Autoencoder (MALA). Firstly, the masked autoencoder (MAE) and Pseudo Label Prediction Module (PLPM) are utilized to recover and perceive normal image patterns. Subsequently, the encoder weights are frozen for further end-to-end network training to predict anomalous maps directly. Token Balance Module(TBM) facilitates anomalous perception and improves anomaly segmentation. By utilizing the Visual Transformer and employing image inpainting as a proxy task, remarkable generalization results are achieved. The proposed method demonstrates its applicability across diverse styles of industrial products. Experiments are conducted on MVTech AD, VisA, KolektorSDD2, and MT datasets, achieving state-of-the-art results in multi-task anomaly detection and segmentation tasks. Specifically, we obtain image AUROC of 98.% and pixel AUROC of 97.1% on the MVTech AD dataset, pixel AUROC of 97.1% on the VisA dataset, and pixel AUROC of 98.7% on the KolektorSDD2 dataset. Defect localization self-supervised learning visual transformer (ViT) masked autoencoder (MAE) industrial products Electrical engineering. Electronics. Nuclear engineering Haolong Peng verfasserin aut Le Huang verfasserin aut Jianming Zhang verfasserin aut Wei Jiang verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 127526-127538 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:127526-127538 https://doi.org/10.1109/ACCESS.2023.3332475 kostenfrei https://doaj.org/article/18b6318a0a3d44ec854e8808a830b8a1 kostenfrei https://ieeexplore.ieee.org/document/10316283/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 127526-127538
allfieldsSound	10.1109/ACCESS.2023.3332475 doi (DE-627)DOAJ09268470X (DE-599)DOAJ18b6318a0a3d44ec854e8808a830b8a1 DE-627 ger DE-627 rakwb eng TK1-9971 Yibo Chen verfasserin aut A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Despite significant advancements in self-supervised anomaly detection, multi-class anomaly detection tasks still pose substantial challenges. Most existing methods require individual network training for each category of objects. This paper presents a novel end-to-end approach for multi-class anomaly detection: self-supervised Mask-pretrained Anomaly Localization Autoencoder (MALA). Firstly, the masked autoencoder (MAE) and Pseudo Label Prediction Module (PLPM) are utilized to recover and perceive normal image patterns. Subsequently, the encoder weights are frozen for further end-to-end network training to predict anomalous maps directly. Token Balance Module(TBM) facilitates anomalous perception and improves anomaly segmentation. By utilizing the Visual Transformer and employing image inpainting as a proxy task, remarkable generalization results are achieved. The proposed method demonstrates its applicability across diverse styles of industrial products. Experiments are conducted on MVTech AD, VisA, KolektorSDD2, and MT datasets, achieving state-of-the-art results in multi-task anomaly detection and segmentation tasks. Specifically, we obtain image AUROC of 98.% and pixel AUROC of 97.1% on the MVTech AD dataset, pixel AUROC of 97.1% on the VisA dataset, and pixel AUROC of 98.7% on the KolektorSDD2 dataset. Defect localization self-supervised learning visual transformer (ViT) masked autoencoder (MAE) industrial products Electrical engineering. Electronics. Nuclear engineering Haolong Peng verfasserin aut Le Huang verfasserin aut Jianming Zhang verfasserin aut Wei Jiang verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 127526-127538 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:127526-127538 https://doi.org/10.1109/ACCESS.2023.3332475 kostenfrei https://doaj.org/article/18b6318a0a3d44ec854e8808a830b8a1 kostenfrei https://ieeexplore.ieee.org/document/10316283/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 127526-127538
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callnumber-first	T - Technology
author	Yibo Chen
spellingShingle	Yibo Chen misc TK1-9971 misc Defect localization misc self-supervised learning misc visual transformer (ViT) misc masked autoencoder (MAE) misc industrial products misc Electrical engineering. Electronics. Nuclear engineering A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method
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topic_title	TK1-9971 A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method Defect localization self-supervised learning visual transformer (ViT) masked autoencoder (MAE) industrial products
topic	misc TK1-9971 misc Defect localization misc self-supervised learning misc visual transformer (ViT) misc masked autoencoder (MAE) misc industrial products misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Defect localization misc self-supervised learning misc visual transformer (ViT) misc masked autoencoder (MAE) misc industrial products misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Defect localization misc self-supervised learning misc visual transformer (ViT) misc masked autoencoder (MAE) misc industrial products misc Electrical engineering. Electronics. Nuclear engineering
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title	A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method
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title_full	A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method
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title_sort	novel mae-based self-supervised anomaly detection and localization method
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title_auth	A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method
abstract	Despite significant advancements in self-supervised anomaly detection, multi-class anomaly detection tasks still pose substantial challenges. Most existing methods require individual network training for each category of objects. This paper presents a novel end-to-end approach for multi-class anomaly detection: self-supervised Mask-pretrained Anomaly Localization Autoencoder (MALA). Firstly, the masked autoencoder (MAE) and Pseudo Label Prediction Module (PLPM) are utilized to recover and perceive normal image patterns. Subsequently, the encoder weights are frozen for further end-to-end network training to predict anomalous maps directly. Token Balance Module(TBM) facilitates anomalous perception and improves anomaly segmentation. By utilizing the Visual Transformer and employing image inpainting as a proxy task, remarkable generalization results are achieved. The proposed method demonstrates its applicability across diverse styles of industrial products. Experiments are conducted on MVTech AD, VisA, KolektorSDD2, and MT datasets, achieving state-of-the-art results in multi-task anomaly detection and segmentation tasks. Specifically, we obtain image AUROC of 98.% and pixel AUROC of 97.1% on the MVTech AD dataset, pixel AUROC of 97.1% on the VisA dataset, and pixel AUROC of 98.7% on the KolektorSDD2 dataset.
abstractGer	Despite significant advancements in self-supervised anomaly detection, multi-class anomaly detection tasks still pose substantial challenges. Most existing methods require individual network training for each category of objects. This paper presents a novel end-to-end approach for multi-class anomaly detection: self-supervised Mask-pretrained Anomaly Localization Autoencoder (MALA). Firstly, the masked autoencoder (MAE) and Pseudo Label Prediction Module (PLPM) are utilized to recover and perceive normal image patterns. Subsequently, the encoder weights are frozen for further end-to-end network training to predict anomalous maps directly. Token Balance Module(TBM) facilitates anomalous perception and improves anomaly segmentation. By utilizing the Visual Transformer and employing image inpainting as a proxy task, remarkable generalization results are achieved. The proposed method demonstrates its applicability across diverse styles of industrial products. Experiments are conducted on MVTech AD, VisA, KolektorSDD2, and MT datasets, achieving state-of-the-art results in multi-task anomaly detection and segmentation tasks. Specifically, we obtain image AUROC of 98.% and pixel AUROC of 97.1% on the MVTech AD dataset, pixel AUROC of 97.1% on the VisA dataset, and pixel AUROC of 98.7% on the KolektorSDD2 dataset.
abstract_unstemmed	Despite significant advancements in self-supervised anomaly detection, multi-class anomaly detection tasks still pose substantial challenges. Most existing methods require individual network training for each category of objects. This paper presents a novel end-to-end approach for multi-class anomaly detection: self-supervised Mask-pretrained Anomaly Localization Autoencoder (MALA). Firstly, the masked autoencoder (MAE) and Pseudo Label Prediction Module (PLPM) are utilized to recover and perceive normal image patterns. Subsequently, the encoder weights are frozen for further end-to-end network training to predict anomalous maps directly. Token Balance Module(TBM) facilitates anomalous perception and improves anomaly segmentation. By utilizing the Visual Transformer and employing image inpainting as a proxy task, remarkable generalization results are achieved. The proposed method demonstrates its applicability across diverse styles of industrial products. Experiments are conducted on MVTech AD, VisA, KolektorSDD2, and MT datasets, achieving state-of-the-art results in multi-task anomaly detection and segmentation tasks. Specifically, we obtain image AUROC of 98.% and pixel AUROC of 97.1% on the MVTech AD dataset, pixel AUROC of 97.1% on the VisA dataset, and pixel AUROC of 98.7% on the KolektorSDD2 dataset.
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title_short	A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method
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author2	Haolong Peng Le Huang Jianming Zhang Wei Jiang
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