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.

翻译：尽管工业异常检测技术在近年来取得了显著进展，但生成逼真的异常样本以及学习正常样本的先验知识仍然是具有挑战性的任务。本研究提出了一种名为FractalAD的端到端工业异常检测方法。该方法通过从正常样本中合成分形图像与分形块来获取训练样本。这种分形异常生成方法旨在对异常的全形态进行采样。此外，我们设计了一种骨干知识蒸馏结构，用于提取正常样本中包含的先验知识。利用余弦相似度注意力模块，将教师模型与学生模型之间的差异转换为异常注意力。所提出的方法使得端到端语义分割网络能够在无需向骨干网络和分割头添加任何可训练参数的情况下用于异常检测，并且在训练和推理速度上相较于其他方法具有明显优势。消融实验结果证实了分形异常生成与骨干知识蒸馏的有效性。性能实验结果表明，与现有的其他先进异常检测方法相比，FractalAD在MVTec AD数据集和MVTec 3D-AD数据集上均取得了具有竞争力的结果。