Steel Defect Detection with YOLO-RSD: Integrating Texture Feature Enhancement and Environmental Noise Exclusion

Real-time industrial inspection is a crucial component of production automation, with key challenges lying in enhancing detection accuracy for specific tasks and effectively mitigating the adverse impacts of complex production environments. Addressing these issues, this paper proposes an innovative solution. We introduce the Head_DySnake module in the detection head to significantly bolster the capture and recognition capabilities of defect texture features. Concurrently, at the initial stage of the backbone network, we integrate the Attention Denoising Module(ADConv) module, which employs an attention-guided mechanism for effective noise reduction in production environments, thereby eliminating high-level noise caused by high background similarity. Through these optimizations, our research achieves a 6.3% mAP improvement on the NEU-DET dataset with a computational demand of merely 9.8 GFLOPs, and a 5.9% mAP improvement on the GC10-DET dataset. This study thoroughly explores the impact of steel defect-specific textures on recognition performance and validates the positive role of attention-guided environmental denoising strategies in enhancing model robustness. These findings offer new perspectives for lightweight model design and performance optimization in industrial production inspection and are expected to provide valuable insights for the detection of other types of defects in related research fields.