Scholarlink

<p>Current you only look once (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 propose a new method, which combined the lightweight network mobile vision transformer (MobileViT) with the convolutional block attention module (CBAM) 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 enhanced 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2025年7月11日

查看全文
  • Yuchen Liu,
  • Fuzheng Liu,
  • Mingshun Jiang

摘要

Current you only look once (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 propose a new method, which combined the lightweight network mobile vision transformer (MobileViT) with the convolutional block attention module (CBAM) 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 enhanced 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.