  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.
