Surface Defect Detection of Lithium Battery Electrodes Based on Improved Unet Network

In order to avoid the problems of life shortening or safety accidents caused by the surface defects of lithium batteries, it is necessary to study an efficient and accurate methods for lithium battery electrode plate defect detection. In this research, the simple Unet semantic segmentation network is used to detect defects of lithium battery. In order to improve the segmentation accuracy, first the coding structure in the original network is replaced with VGG16, which is similar to the Unet coding structure, to obtain the pre-training weights had been trained. Then, feature fusion module of the simply fusion pyramid network (SFPN) is added to the skip connection of the Unet network to avoid large information differences between feature maps. Finally, label smoothing is applied to optimize the loss function to prevent the network from overfitting. Through experimental verification, the accuracy of the semantic segmentation network optimized by the proposed method is improved to 93.70%, and the probability of false segmentation and segmentation discontinuity is significantly reduced. This optimization process has certain practical value.