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.