This paper proposes a surface defect detection model on a copper plat e 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 a nd random positions, a big data-driven deep learning strategy is adopted.Using the copper strip surfa ce defect image as the training sample,the YOLOv4target 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 origin al YOLOv4. The model has an average accuracy of 91.46% for all categories and a detection s peed of 49frames per second.Compared with the two-stage detection model faster region-based convolutional neural network (Faster R-CNN),it can i mprove the detection speed while ensuring the detection accuracy,which can meet the needs of online detection.Defect detection task in industria l production process is suitable for completing copper strips.