【摘要】 针对目前工业生产过程中存在砂纸表面缺陷人工质量检测精度低和检测效率低问题，提出一种基于YOLOv5网络模型融合CA注意力机制的砂纸表面缺陷自动检测方法。首先对砂纸生产过程中的砂纸表面图像进行采样，将收集到的砂纸表面缺陷图像分成脱砂、堆砂、划痕和褶皱4种缺陷类型来制作砂纸表面缺陷数据集；其次将YOLOv5主干网络中的C3模块与CA注意力机制结合，改进为CAC3模块；最后将改进前后的网络模型在自建砂纸表面缺陷数据集上进行训练和验证。实验结果表明：得到改进后的YOLOv5+CAC3网络模型，其P、R、mAP@0.5、mAP@0.5:0.95和S的数值分别为96.2%,92.9%,95.8%,65.0%,16.8 ms,相比于改进前的YOLOv5网络模型分别提高了1.1%、2.2%、0.6%、1.7%、4.5 ms。该方法在砂纸表面缺陷检测中精度高、速度快、检测稳定，符合砂纸生产过程中砂纸表面缺陷检测的要求。更多还原

【Abstract】 Aiming at the problems of low accuracy and low detection efficiency of manual quality detection of sandpaper surface defects in the current industrial production process, an automatic detection method of sandpaper surface defects based on the YOLOv5 network model and CA attention mechanism is proposed. Firstly, the surface images of sandpaper in the process of sandpaper production are sampled, and the collected surface defect images are divided into four defect types, namely, sand removal, sand piling, scratch, and fold, to make the surface defect datasets of sandpaper. Secondly, the C3 module in the YOLOv5 backbone network is improved to the CAC3 module by combining it with the CA attention mechanism. Finally, the network models before and after the improvement are trained and verified on the self-built sandpaper surface defect datasets. The experimental results show that the values of P, R, mAP@0.5, mAP@0.5:0.95, and S of the improved YOLOv5+CAC3 network model are 96.2%, 92.9%, 95.8%, 65.0%, and 16.8 ms, which are 1.1%, 2.2%, 0.6%, 1.7% and 4.5 ms higher than the YOLOv5 network model before improvement. This method has high precision, fast speed, and stable detection in the detection of surface defects of sandpaper, which meets the requirements of the detection of surface defects of sandpaper in the production process.更多还原