According to QYResearch, a global market research firm, the global market size of secondary batteries is growing at an average annual rate of 8.1%, but fires and casualties continue to occur due to the lack of quality and reliability of secondary batteries. Therefore, improving the quality of secondary batteries is a major factor in determining a company’s competitive advantage. In particular, lead taps, which electrically connect the negative and positive electrodes of secondary batteries, are a key factor in determining the stability of the battery. Currently, the quality inspection of secondary battery lead tab manufacturers mostly consists of visual inspection after vision inspection with a rule-based algorithm, which has limitations on the types of defects that can be detected, and the inspection time is increasing due to overlapping inspections, which is directly related to productivity. Therefore, this study aims to automate the quality inspection of lead tabs of secondary batteries by applying deep-learning-based algorithms to improve inspection accuracy, improve reliability, and improve productivity. We selected the YOLOv5 model, which, among deep-learning algorithms, has a benefit for object detection, and used the YOLOv5_CBAM model, which replaces the bottleneck part in the C3 layer of YOLOv5 with the Convolutional Block Attention Module (CBAM) based on the attention mechanism, to improve the accuracy and speed of the model. As a result of applying the YOLOv5_CBAM model, we found that the parameter was reduced by more than 50% and the performance was improved by 2%. In addition, image processing was applied to help segment the defective area to apply the SPEC value for each defective object after detection.