基于多尺度边缘增强和改进的YOLOv10改进的印刷电路板的小缺陷检测
Weixun Chen1, Xuneng Ke2, Siming Meng2
1The College of Artificial Intelligence, Guangzhou Railway Polytechnic, Guangzhou, 510430, China. chenweixun@gtxy.edu.cn.
Scientific reports
|October 17, 2025
概括
这项研究引入了一个新的MS-YOLO网络,用于检测小型印刷电路板 (PCB) 的缺陷. 它通过较小的模型尺寸实现了高精度和效率,提高了PCB制造质量.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 制造业 制造技术 制造技术
背景情况:
- 印刷电路板 (PCB) 制造的复杂性导致影响性能的缺陷.
- 由于低分辨率和小目标尺寸,准确识别微小的PCB缺陷是具有挑战性的.
研究的目的:
- 开发一个新的YOLO MSES-SPDConv (MS-YOLO) 网络,以准确高效地检测PCB的轻微缺陷.
- 为了实现更小的模型尺寸,同时提高检测性能.
主要方法:
- 引入了多层次边缘增强 (MSES),以取代YOLOv10的C2F模块,以增强功能学习.
- 嵌入了改进的SPDConv在网络子中,用于多级特征提取和减少信息丢失.
- 优化了使用层级自适应量基于修剪 (LAMP) 和双蒸策略的网络.
主要成果:
- 拟议的MS-YOLO网络实现了98.9%的平均平均精度 (mAP@50).
- 在PCB缺陷检测方面,与一些最先进的模型相比,表现出更高的性能.
- 在检测准确度,操作效率和模型尺寸缩小方面取得了显著的改进.
结论:
- 该MS-YOLO网络有效地解决了检测PCB轻微缺陷的挑战.
- 提出的方法增强特征学习,减少信息丢失,并优化模型大小.
- MS-YOLO为改善PCB制造质量控制提供了实际应用.
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