Deep learning technology has injected new vitality into the defect detection and disassembly recycling of batteries. This paper describes the defect detection and disassembly recycling of batteries. Firstly, this paper outlines a protocol for detecting imperfections that relies on traditional machine vision procedures, encompassing both image domain analysis and transform domain analysis. Secondly, this paper describes the applications of deep learning in defect detection, including supervised training, unsupervised data mining, limitedly supervised learning, etc. Then, deep neural network applications in the disassembly and recycling of waste batteries is described. Finally, defect inspection relying on traditional machine vision methodologies, the approach for anomaly detection utilizing hierarchical learning, and the implementation of deep neural networks technology in the disassembly and recycling of waste batteries are all summarized herein.