Research on YOLO algorithm for lightweight PCB defect detection based on MobileViT

Abstract:

Current YOLO-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board (PCB) defect detection application scenario. In order to solve this problem, we proposed a new method, which we combined the lightweight network MobileViT with the CBAM attention mechanism and the new regression loss function. This method needed less computation resources, making it more suitable for embedded edge detection devices. Meanwhile, the new loss function improved the positioning accuracy of the bounding box and en-hanced the robustness of the model. In addition, experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9% across six typical defect detection tasks, while reducing computational costs by nearly 90%. It significantly reduces the model's computational requirements while maintaining accuracy, ensuring reliable performance for edge deployment.

Key words:YOLO; Printed circuit board; Lightweight; Defect detection;