A CRS-YOLO algorithm based on flexible printed circuit board target detection is proposed to solve the problem that surface defects of flexible printed circuit board are small in size and not obvious in feature, and the exist- ing detection schemes are poor in real-time. On the basis of the single-stage network YOLOv5 model, CA attention mechanism module is first added to Backbone to strengthen the feature extraction capability. Secondly, the SPP pool pyramid is replaced by the Basic RFB pool pyramid with better performance to enlarge the receptive field and reduce the missing rate of the model. At last, SIoU loss function is used to replace CIoU loss function to accelerate the conver- gence of the training model and improve the detection ability of the model. The experimental results show that under the verification of the defect data set of the flexible printed circuit board, the mAP of the CRS-YOLO algorithm is 9% higher than that of the original network model, the detection speed is greatly improved, and the FPS is up to 48, which meets the accuracy and real-time detection of the surface defects of the flexible printed circuit board.