CBAM-ConvNeXt-UAD: A Knowledge Distillation and Gradient-aware Approach for Unsupervised Industrial Defect Detection

Surface defect detection is pivotal for industrial quality control. While supervised methods depend on large annotated datasets, their practicality is often limited by the scarcity of defective samples, high annotation costs, and the extensive diversity of defect types. By contrast, unsupervised approaches have thus gained increasing research attention as it requires only normal samples for training. This study presents a ConvNeXt-based knowledge distillation anomaly detection model designed for high-precision industrial inspection. The proposed method builds upon a teacher-student knowledge distillation framework with a pre-trained ConvNeXt-Tiny backbone, enabling unsupervised learning by modeling the feature distribution of normal samples. Within this architecture, the teacher network remains fixed to provide feature priors, while the student network incorporates Convolutional Block Attention Modules (CBAM) to enhance representational capacity. Furthermore, a joint optimization strategy combining Gradient-aware loss and Mean Squared Error (MSE) is introduced. Augmented with spatial structure constraints, this approach significantly improves segmentation and localization accuracy, allowing defect regions to be automatically identified based on feature residuals—without the need for manual labels. Additionally, a category-adaptive score aggregation mechanism based on Top-K pooling is designed to accommodate diverse defect characteristics. Experimental evaluations on the MVTec AD benchmark show that the proposed model achieves a Detection AUC of 94.9% and a Segmentation AUC of 96.9%, demonstrating competitive performance across both object and texture categories and confirming the efficacy of the proposed method.