This paper proposes a surface defect detection model on a copper plate and strip based on YOLOv4.Aiming at the problem of surface defects in the production process of copper metal plate and strip that are difficult to locate and identify due to their various forms and random positions, a big data-driven deep learning strategy is adopted.Using the copper strip surface defect image as the training sample,the YOLOv4 target detection model is trained.The experimental results show that the improved model recognizes the copper strip surface defect with a full-category mean average precision (mAP) of 93.37%,which is higher than the original YOLOv4.The model has an average accuracy of 91.46% for all categories and a detection speed of 49 frames per second.Compared with the two-stage detection model faster region-based convolutional neural network (Faster R-CNN),it can improve the detection speed while ensuring the detection accuracy,which can meet the needs of online detection.Defect detection task in industrial production process is suitable for completing copper strips.