Although industrial anomaly detection (AD) technology has made significant progress in recent years, generating realistic anomalies and learning priors knowledge of normal remain challenging tasks. In this study, we propose an end-to-end industrial anomaly segmentation 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. 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 compared with other state-of-the-art anomaly detection methods.

翻译：虽然近年来工业异常探测技术取得了显著进展,但产生了现实的异常现象,并学习了对正常情况的了解,这仍然是一项具有挑战性的任务。在本研究中,我们建议采用一个名为FractalAD的末端至端工业异常分离法。培训样本是通过合成分形图象和正常样品的补丁获得的。这种分形异常生成法旨在对异常情况的全部形态进行取样。此外,我们设计了一个主干知识蒸馏结构,以提取正常样品中包含的先前知识。教师和学生模型之间的差异被转换成异常现象注意,使用一个cosine相似的注意模块。拟议方法使得终端到端的语义分离网络能够用于异常现象的检测,而不会给骨干和分形头添加任何可训练的参数。通缩研究的结果证实了分形异常生成和骨干知识蒸馏的有效性。绩效实验结果表明,FractalAD在MVTec AD数据集与其他状态异常探测方法相比取得了竞争性的结果。