Abstract
Steel surface defect detection is critical for ensuring the reliability and safety of automotive manufacturing. However, existing methods often suffer from high computational cost, weak sensitivity to fine textures, and limited adaptability to diverse defect scales. To address these challenges, we propose WaveMamba-YOLO, a real-time detection framework that integrates frequency-domain enhancement with efficient state-space modeling. The architecture introduces three key modules: (1) CHDWT, which combines Haar wavelet decomposition and residual learning to preserve structural details during downsampling; (2) GLaM, a global-local-aware Mamba module that couples large-kernel convolution with state-space modeling to capture long-range dependencies at linear complexity; and (3) LWGA, a lightweight group attention mechanism that adaptively attends to micro-, regular-, medium-, and large-scale defects. Experiments on the Severstal Steel Defect and NEU-DET datasets demonstrate that WaveMamba-YOLO achieves superior performance, reaching 51.70% mAP@0.5 and 58.60% precision on Severstal and 77.70% mAP@0.5 on NEU-DET, consistently surpassing mainstream lightweight detectors. These results confirm the effectiveness of WaveMamba-YOLO in balancing detection accuracy and efficiency, highlighting its potential for real-time industrial inspection.