An Improved YOLOv5 with Structural Reparameterization for Surface Defect Detection

Surface defects produced by the manufacturing process directly degrades the quality of industrial materials such as hot-rolled steel. However, existing methods for detecting surface defects cannot meet the requirements in terms of speed and accuracy. Based on structural re-parameterization, coordinate attention (CA) mechanism, and an additional detection head, we propose an improved YOLOv5 model for detecting surface defects of steel plates. Firstly, using the technique of structural re-parameterization in RepVGGBlock, the multi-channel structure of the training backbone network is converted to a single-channel structure of the inference network. This allows the network to speed up its inference while maintaining detection accuracy. Secondly, CA is integrated into the detection head to further improve detection accuracy. Finally, a layer of detection head is added at the end of the network to focus on detecting small targets. The experimental results on the Northeastern University (NEU) surface defect database show that, our model is superior to the state-of-the-art detectors, such as the original YOLOv5, Fast-RCNN in accuracy and speed.