YOLOv13 Steel Surface Defect Detection Method Based on Multi-Scale Denoising Enhanced A2C2f Module

Steel surface quality critically determines the service safety and structural reliability of industrial products. Defects such as cracks, inclusions, patches, pitting, rolled-in scale, and scratches severely compromise product safety, making accurate and efficient detection a key step in quality control. However, the native A2C2f module in YOLOv13 exhibits insufficient multi-scale feature extraction for tiny defects and weak robustness under complex industrial backgrounds, hindering the detection of these six defect types. To address these gaps, we propose a multi-scale denoising enhanced module, A2C2f-MSDE, which constructs a multi-scale multi-kernel fusion branch (MSKF) with learnable adaptive weights, integrates a lightweight SEL channel attention and a DE denoising module, and employs a dual learnable residual scaling structure, while preserving the original multi-scale fusion architecture. We embed A2C2f-MSDE into the YOLOv13 backbone, perform ablation studies to verify each component's contribution, compare it with mainstream advanced detectors on the public NEU-DET dataset, and conduct generalization tests on the GC10-DET dataset. Experiments on NEU-DET show that the improved YOLOv13n achieves mAP50-95 of 0.454 (9.4% relative gain over baseline, absolute gain 0.039), with mAP50 and mAP75 reaching 0.774 and 0.466, at an inference speed of 555 FPS, respectively, outperforming the compared mainstream models. On GC10-DET, mAP50 reaches 0.704, comparable to the baseline, maintaining stable overall detection capability, while mAP75 and mAP50-95 improve by 0.033 and 0.019, verifying the module's performance advantages under high localization accuracy requirements and its cross-dataset generalization ability. The proposed module effectively balances detection accuracy and lightweight characteristics, providing a high-performance solution for industrial steel defect detection.