SD-INPFormer: an unsupervised anomaly detection model for steel surface defect

Accurate detection of steel-surface defects is essential for industrial quality control. However, scarcity of defective samples, pronounced morphological variability, and prohibitive annotation costs severely limit the performance of conventional supervised approaches. To address this limitation, we propose SD-INPFormer, an unsupervised anomaly detection model built upon an enhanced INPFormer architecture. By dynamically extracting intrinsic normal prototypes (INPs) from each test image, the model entirely circumvents the need for external annotations. Specifically, we propose an adaptive weighted multi-scale feature fusion mechanism that preserves defect-related cues across all encoder scales. We introduce the MambaMixer module to suppress background texture noise and to amplify subtle defect responses. A MemoryMLP is further proposed to prevent the encoding of anomalous features, while multi-scale gated cross attention is employed to extract INPs at multiple scales. In addition, we devise multi-centre prototype attention to encompass diverse defect patterns. SD-INPFormer’s excellent performance is empirically validated on the SteelAD and SeverstalAD datasets, providing a high-precision solution for automated steel-surface quality inspection. The implementation is publicly available at https://github.com/ghlerrix/SD-INPformer.