FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation

While industrial anomaly detection (AD) technology has experienced notable advancements in recent years, the challenges of generating realistic anomalies and acquiring a deep understanding of normal patterns persist. In this research, we introduce FractalAD, an end-to-end industrial anomaly detection method. The training dataset is curated by synthesizing fractal images and patches derived from normal samples. This unique fractal anomaly generation method is meticulously crafted to capture the intricate morphology of anomalies comprehensively. Furthermore, we have devised a knowledge distillation structure, aimed at extracting inherent knowledge present in normal samples. To bridge the gap between a teacher and a student model, discrepancies are translated into anomaly attention through a cosine similarity attention module. Notably, our proposed method facilitates the utilization of an end-to-end semantic segmentation network for anomaly detection, seamlessly integrating into the existing framework without introducing additional trainable parameters to the backbone and segmentation head. It exhibits clear advantages over alternative methods in terms of both training efficiency and inference speed. The outcomes of ablation studies underscore the efficacy of both fractal anomaly generation and backbone knowledge distillation. Performance experiments substantiate the competitive standing of FractalAD, showcasing commendable results on the MVTec AD dataset and MVTec 3D-AD dataset when compared to other state-of-the-art anomaly detection methods.