Defect detection method of lithium battery based on improved YOLOv7

For the traditional algorithm to detect lithium battery defects, the missing rate is high and the speed is slow, an improved YOLOv7 algorithm was proposed. Firstly, CBAM attention mechanism is added to feature extraction part, which can enhance network's representation ability. Secondly, in the feature fusion part, ConvNeXt lightweight module was used to replace the original ELAN module to reduce the model's complexity. Finally, SPD module is added before the detection head at the output end to increase focus on smaller goals with surface defects of lithium batteries at the output end. The results show that the optimization algorithm can improve the accuracy and speed of the lithium battery. The proposed algorithm achieves a 92.7% detection accuracy, surpassing the original network by 2.1%.