Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

Huang, Chaoqin; Xu, Qinwei; Wang, Yanfeng; Wang, Yu; Zhang, Ya

doi:10.1109/tmm.2022.3175611

Cited by 35 publications

(14 citation statements)

References 59 publications

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“…[82] L2 -The paper proposes to reconstruct the anomalous area differently from the original image. [83] L2, SSIM, GMS -Similar to I3AD, but the paper adds skip connections to reconstruction network. DREAM [84] L2, SSIM, Focal -The paper designs a method to generate abnormal images and uses U-Net to distinguish anomalies after reconstruction.…”

Section: Vggmentioning

confidence: 99%

See 1 more Smart Citation

Deep Industrial Image Anomaly Detection: A Survey

Liu¹,

Xie²,

Wang³

et al. 2023

Preprint

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The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

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Section: Vggmentioning

confidence: 99%

“…I3AD improves reconstruction quality by only reconstructing inpainting masks over images, and only masking regions with a high probability of abnormality. SSM [83] is conceptually similar to I3AD. SSM adds skip-connections to the reconstruction network and predicts the mask region as the training target.…”

Section: Autoencodermentioning

confidence: 99%

Deep Industrial Image Anomaly Detection: A Survey

Liu¹,

Xie²,

Wang³

et al. 2023

Preprint

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show abstract

“…Contrastive learning has recently emerged as a very effective method in self‐supervised learning in computer vision. In these studies [30, 31], they adopted the method of contrastive learning in specific downstream tasks. The key idea of contrastive learning is to bring similar data closer and push data with large differences in similarity farther.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised detection of contrast enhanced highlight landmarks

Fan

et al. 2023

IET Computer Vision

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In the field of landmark detection based on deep learning, most of the research utilise convolutional neural networks to represent landmarks, and rarely adopt Transformer to represent and encode landmarks. Meanwhile, many works focus on modifying the network structure to improve network performance, and there is little research on the distribution of landmarks. In this article,the authors propose an unsupervised model to extract landmarks of objects in images. First, Transformer structure is combined with the convolutional neural network structure to represent and encode the landmarks; next, positive and negative sample pairs between landmarks are constructed, so that the semantically consistent landmarks on the image are pulled closer in the feature space and the semantically inconsistent landmarks are pushed farther in the feature space; then the authors concentrate their attention on the most active points to distinguish the landmarks of an object from the background; finally, based on the new contrastive loss, the network reconstructs the image by the landmarks of the object that are continuously learnt during training. Experiments show that the proposed model achieves better performance than other unsupervised methods on the CelebA, Annotated Facial Landmarks in the Wild, 300W datasets.

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“…However, since the defect location is random and unpredictable in the actual detection, the key to fixing the problem is to utilise fewer masks to completely cover the defect. Huang et al 23 used eight checkerboard masks of different scales to fully reconstruct the test image. The approach modified the iterative mask based on the loss value fed back by the model, progressively revealing the normal area, and eventually locating the abnormal area.…”

Section: Related Workmentioning

confidence: 99%

Progressive mask‐oriented unsupervised fabric defect detection under background repair

Tang,

Jin,

Dai

et al. 2023

Coloration Technology

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Detection of defects is an essential quality control method in fabric production. Unsupervised deep learning‐based reconstruction algorithms have recently been deeply concerned owing to scarce fabric defect samples, high annotation cost, and deficient prior knowledge. Most unsupervised reconstruction models are prone to overfitting and poor generalisation performance, resulting in blurred images, residual defects, and uneven textures in the reconstruction results. On this account, an unsupervised fabric surface defect detection method using the Progressive Mask Repair Model (PMRM) has been developed. Specifically, PMRM with transformer architecture gathers detailed feature information. In order to pay closer attention to the textural properties of fabrics, the model incorporates structural similarity as a constraint in the training stage. In the detection stage, we designate the non‐defective area of the fabric image as the background and the defective area as the foreground. Next, a progressive mask is applied to repair the background of the defective area, which avoids defect false detection resulting from the poor reconstruction effect of the traditional reconstruction model in the non‐defective area. Finally, image processing methods such as image difference, frequency‐tuned salient detection, and threshold binarisation are used to segment the defects. Relative to the other six unsupervised defect detection methods, the proposed scheme increases the F1 score and intersection over union (IoU) by at least 9.34% and 8.49%, respectively. According to the earlier results, PMRM is effective and exhibits superiority.

show abstract

Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

Cited by 35 publications

References 59 publications

Deep Industrial Image Anomaly Detection: A Survey

Deep Industrial Image Anomaly Detection: A Survey

Unsupervised detection of contrast enhanced highlight landmarks

Progressive mask‐oriented unsupervised fabric defect detection under background repair

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