An improved YOLOv7 algorithm for laser welding defect detection

Recently, with the development of new energy vehicles, there has been an increasing demand for new energy batteries. During the production process of batteries, laser welding is an important technology, which can produce defects inevitably. So, the defects detection of laser welding is absolutely necessary to ensure the quality of the batteries. In this paper, we proposed an improved YOLOv7 algorithm for laser welding defect detection of battery pole. The algorithm adopts Conv2former to improve the model's capability to extract contextual information and introduces the CBAM attention mechanism to extract key information. The loss function is also modified to WIoU to further improve the accuracy of the model. The experiment results on the battery pole dataset show that the improved YOLOv7 achieved a 3.1% increase in map@0.5, reaching 0.921, and a 2.1% increase in map@0.5:0.95, reaching 0.682 compared to the original YOLOv7.