A novel dual-student reverse knowledge distillation method for magnetic tile defect detection

Magnetic tiles surface defect detection is crucial in industrial production. However, it is difficult to effectively detect and locate defective areas in magnetic tiles due to the following problems: (1) The defect texture of the magnetic tile material is highly similar to the background texture; (2) The image contrast between the normal and defective areas of the magnetic tile is low; (3) The size and morphology of the defects of the magnetic tile vary greatly. To address the above problems, this study proposes a novel dual-student reverse knowledge distillation framework based on reverse distillation called binary struct and detail reverse distillation (BSDRD). In this framework, a pre-trained teacher network that uses a deep learning model to learn multi-scale and multi-level feature representations serves as the feature extractor. The obtained features are processed by two student networks with different responsibilities. Specifically, the struct student dynamically fuses and compresses multi-scale features. The detail student applies wavelet transform to decompose high-level features into low-frequency and high-frequency components, and this decomposition not only retains global structural information of the high-level features but also enhances the detection ability for complex textures, gradients, and irregular defects. In addition, this paper introduces a multi-dimensional feature gated fusion loss (MD-GFLoss) to improve the model's selectivity for key features and sensitivity to abnormal areas. Experiments on the magnetic tile defect detection dataset show that the proposed BSDRD is particularly effective in handling complex textures and small defects. It outperforms existing methods in both pixel-level and sample-level anomaly detection tasks.