基于YOLOv5的印刷电路板组件的缺陷检测
Minghui Shen1,2, Yujie Liu1,3, Jing Chen1
1School of Computing and Information Technology, Hefei University of Technology, Xuancheng, 242000, China.
Scientific reports
|August 20, 2024
概括
本研究介绍了PCBA-YOLO,这是一个改进的YOLOv5模型,用于印刷电路板组件 (PCBA) 缺陷检测. 它实现了高精度和实时性能,提高了制造效率.
科学领域:
- 计算机视觉 计算机视觉
- 制造业 制造技术 制造技术
- 人工智能的人工智能
背景情况:
- 印刷电路板组件 (PCBA) 缺陷检测对于制造效率至关重要.
- 现有的方法可能缺乏实时应用所需的准确性和速度.
- YOLOv5架构为对象检测任务提供了坚实的基础.
研究的目的:
- 提出一种改进的基于YOLOv5的方法,PCBA-YOLO,用于增强PCBA缺陷检测.
- 提高PCBA制造中自动缺陷识别的准确性和效率.
- 引入一个新的数据集,PCBA-DET,用于培训和评估PCBA缺陷检测模型.
主要方法:
- 修改YOLOv5子网络,将空间金字塔聚合模块替换为跨阶段的部分结构,用于多级特征捕获.
- 将大型内核卷积集成到骨干网络中,以扩大有效的受体场,同时最大限度地降低计算成本.
- 利用SIoU损失函数通过结合角度成本来加速模型的融合.
主要成果:
- 拟议的PCBA-YOLO方法在PCB缺陷数据集上显示了较低的损失值和更高的精度.
- 在新创建的PCBA-DET数据集上实现了97.3%的平均平均精度 (mAP).
- 实现了每秒322.6的实时检测速度,平衡精度和模型大小.
结论:
- PCBA-YOLO显著提高了PCBA缺陷检测的准确性和效率.
- 该方法与其他YOLO系列检测网络相比,提供了具有竞争力的解决方案.
- 开发的PCBA-DET数据集和源代码已公开供进一步研究使用.
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