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

TL;DR

ProtoAD is a prototype-based neural network that enables fast, end-to-end image anomaly detection and localization without training, using pre-trained features and non-parametric clustering to identify anomalies efficiently.

Contribution

The paper introduces ProtoAD, a novel prototype-based neural network that performs anomaly detection and localization without requiring training, leveraging pre-trained features and prototype clustering.

Findings

Achieves competitive accuracy on MVTec AD and BTAD datasets.

Operates with higher inference speed than state-of-the-art methods.

Does not require training phase, enabling end-to-end detection and localization.

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.