Lithium battery is one of the core components of new energy vehicles. But, the complex manufacturing process of lithium battery inevitably introduces various defects, which seriously affects the quality of products. Therefore, defect detection has become an important part of lithium battery manufacturing process. The machine vision method takes into account the advantages of accuracy and speed, which has been paid much attention to. In this article, the research progress of defect detection methods for lithium battery based on machine vision in recent 15 years is reviewed. Firstly, the common surface defect types of lithium battery are introduced, and the main flow of visual defect detection is clarified. Next, the defect detection method of lithium battery based on traditional image processing is emphasized. The four steps, including image preprocessing, image segmentation, feature extraction and classification recognition, are explained in detail. The advantages and disadvantages of each step are compared. Then, the defect detection methods based on deep learning are summarized according to the classification network, detection network and segmentation network. Afterwards, 10 self-built datasets of lithium battery and performance evaluation index of defect detection are sorted out. Finally, it is pointed out that the defect detection of lithium battery is faced with many technical challenges, and the future work is prospected.