A Prototype-Based Neural Network for Image Anomaly Detection and Localization

• URL: http://arxiv.org/abs/2310.02576v2
• Date: Sat, 25 May 2024 10:29:04 GMT
• Title: A Prototype-Based Neural Network for Image Anomaly Detection and Localization
• Authors: Chao Huang, Zhao Kang, Hong Wu,
• Abstract summary: This paper proposes ProtoAD, a prototype-based neural network for image anomaly detection and localization. First, the patch features of normal images are extracted by a deep network pre-trained on nature images. ProtoAD achieves competitive performance compared to the state-of-the-art methods with a higher inference speed.
• Score: 10.830337829732915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image anomaly detection and localization perform not only image-level anomaly classification but also locate pixel-level anomaly regions. Recently, it has received much research attention due to its wide application in various fields. This paper proposes ProtoAD, a prototype-based neural network for image anomaly detection and localization. First, the patch features of normal images are extracted by a deep network pre-trained on nature images. Then, the prototypes of the normal patch features are learned by non-parametric clustering. Finally, we construct an image anomaly localization network (ProtoAD) by appending the feature extraction network with $L2$ feature normalization, a $1\times1$ convolutional layer, a channel max-pooling, and a subtraction operation. We use the prototypes as the kernels of the $1\times1$ convolutional layer; therefore, our neural network does not need a training phase and can conduct anomaly detection and localization in an end-to-end manner. Extensive experiments on two challenging industrial anomaly detection datasets, MVTec AD and BTAD, demonstrate that ProtoAD achieves competitive performance compared to the state-of-the-art methods with a higher inference speed. The source code is available at: https://github.com/98chao/ProtoAD.

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