GMD-YOLOv5s: An Improved Steel Surface Defect Detection Algorithm Based on YOLOv5s

Current steel surface defect detection methods have the shortcomings of low accuracy, poor real-time performance and limitation to detect small targets. To solve those problems, we proposed a lightweight detection model named GMD-YOLOv5s. Firstly, we introduce the C3 Global Ghost (C3-GGhost) module, an enhancement of the original C3 module, which improves both detection speed and accuracy for industrial applications. Secondly, Multi-Scale Dilated Attention (MSDA) is integrated into the neck network to capture target at different scales. Because MSDA can expand the model's receptive field and feature representation capabilities. Finally, in order to enhance the model's ability to detect small targets, we replace the conventional detection head with a Distributed Pixel-level Feature Refinement Head (DPFRHead). Experimental results show that GMD-YOLOv5s achieves a mean Average Precision (mAP) of 82.9% on the NEU-DET dataset. Our method improves by 30% compared to the baseline YOLOv5, while reducing the number of parameters by 60%. Ablation and comparative experiments validate the model's effectiveness and accuracy in detecting defects on steel surfaces.