AI Detection for Anode Copper Plate Surface Defect

How to quickly and accurately detect the surface defects of objects has always been the focus of computer vision research. In this paper, a defect detection based on AI method for anode copper plate is proposed. Firstly, the physical appearance image data set of anode copper plate is built by ourselves and its physical size parameters are modeled. Secondly, the YOLOv5 target detection network is used to train the image data. Finally, AI detection of surface cracks and other defects of anode copper plate is completed by image segmentation algorithm and comparison with standard template library. The experimental results show that the method presented in this paper has a good detection and recognition effect on the anode copper plate cracks and other defects, which can not only effectively improve the detection accuracy and speed, but also greatly reduce human resources.