RESULTS OF COMPARISON EXPERIMENTS.

Aiming at the problem of low efficiency of manual detection in the field of metal surface defect detection, a deep learning defect detection method based on improved YOLOv5 algorithm is proposed. Firstly, in the feature enhancement part, we replace the multi-head self-attention module of the standard transformer encoder with the EVC module to improve the feature extraction ability. Second, in the prediction part, adding a small target detection head can reduce the negative impact of drastic object scale changes and improve the accuracy and stability of detection. Finally, the performance of the algorithm is verified by ablation experiments and analogy experiments. The experimental results show that the improved algorithm has greatly improved mAP and FPS on the data set, and can quickly and accurately identify the types of metal surface defects, which has reference significance for practical industrial applications.针对金属表面缺陷检测领域人工检测效率低下的问题，本文提出了一种基于改进YOLOv5算法的深度学习缺陷检测方法。首先，在特征增强环节，我们将标准Transformer编码器的多头自注意力模块替换为EVC模块，以提升特征提取能力。其次，在预测阶段，新增小目标检测头以缓解目标尺度剧烈变化带来的负面影响，提升检测的精度与稳定性。最后，通过消融实验与对照实验验证了所提算法的性能。实验结果表明，改进后的算法在数据集上的平均精度均值（mAP）与FPS均得到大幅提升，能够快速且准确地识别金属表面缺陷的类型，对实际工业应用具有参考意义。