In order to meet the requirements of detection accuracy and real-time performance of lithium-ion battery electrode defects, and to solve the problems of complex background noise of electrode images, small defects and low contrast, this paper proposes a electrode defect YOLO detection algorithm based on attention mechanism and multi-scale feature fusion. On the basis of YOLOv4. First, we embed the SE(squeeze-and-excitation) attention module in the feature extraction backbone network to distinguish the importance of different channels in the feature map, strengthen the key features of the target area, and improve the detection accuracy of the network; secondly add the pooling pyramid(ASPP) structure fused with atrous convolution to increase the network receptive field while retaining the multi-scale feature information to the greatest extent, and improve the detection performance of the algorithm for small targets; then design a multi-scale dense feature pyramid, on the basis of the three-scale feature map, a shallow feature is added, and the feature is fused by dense connection, which improves the fusion ability of shallow detail features and high-level semantic information, and enhances the extraction of small defect features; finally, the K-means++ algorithm is used for clustering. In the prior box, the focal loss function is introduced to increase the loss weight of small target samples, which effectively improves the convergence speed of network learning. The experimental results show that the mAP value of the proposed algorithm is increased by 6.42% compared with the original YOLOv4 model, which has a greater advantage in comprehensive performance than other commonly used algorithms, and can better meet the real-time monitoring needs of actual industrial production.