  Although industrial anomaly detection (AD) technology has made significant
progress in recent years, generating realistic anomalies and learning priors of
normal remain challenging tasks. In this study, we propose an end-to-end
industrial anomaly detection method called FractalAD. Training samples are
obtained by synthesizing fractal images and patches from normal samples. This
fractal anomaly generation method is designed to sample the full morphology of
anomalies. Moreover, we designed a backbone knowledge distillation structure to
extract prior knowledge contained in normal samples. The differences between a
teacher and a student model are converted into anomaly attention using a cosine
similarity attention module. The proposed method enables an end-to-end semantic
segmentation network to be used for anomaly detection without adding any
trainable parameters to the backbone and segmentation head, and has obvious
advantages over other methods in training and inference speed.. The results of
ablation studies confirmed the effectiveness of fractal anomaly generation and
backbone knowledge distillation. The results of performance experiments showed
that FractalAD achieved competitive results on the MVTec AD dataset and MVTec
3D-AD dataset compared with other state-of-the-art anomaly detection methods.
