MFE-YOLO:用于PCB缺陷检测的多级特征增强网络,具有跨组注意力和FIoU损失
Ruohai Di1, Hao Fan2, Hanxiao Feng2
1School of Cross-Innovation, Xi'an Technological University, Xi'an 710021, China.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
我们开发了MFE-YOLO,这是一个贝叶斯增强的系统,用于检测印刷电路板 (PCB) 的缺陷,大大减少了错误报警. 该方法通过量化预测不确定性来提高自动检查可靠性,这对于现代制造质量控制至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业自动化 工业自动化
背景情况:
- 制造业的自动化质量控制依赖于印刷电路板 (PCB) 的准确缺陷检测.
- 现有的深度学习模型,如YOLOv5,由于缺乏预测不确定性量化,经常表现出过度自信的错误,导致工业检查中的错误报警.
- 对可靠的自动化检查系统来说,概率性校准是必不可少的.
研究的目的:
- 提出MFE-YOLO,这是一个基于YOLOv5.5的贝叶斯增强检测框架.
- 通过整合不确定性意识机制,提高PCB缺陷检测的准确性和操作可靠性.
- 解决工业PCB检查中小目标和复杂背景的挑战.
主要方法:
- 构建了一个多背景PCB缺陷数据集,以改善模型通用化.
- 集成了卷积块注意模块 (CBAM) 作为一个特征智能的不确定性权衡机制.
- 开发了一种新的概率性FIoU损失函数,用于改进边界框回归和定位不确定性捕获.
主要成果:
- MFE-YOLO实现了最先进的性能,mAP@0.5为93.9%,mAP@0.5:0.95为59.6%,表现优于YOLOv8和EfficientDet.
- 该框架显示了更好地校准的信任分数,减少了虚假警报.
- 在复杂的背景中实现了小缺陷的检测精度和可靠性的提高.
结论:
- MFE-YOLO为高通量PCB检查提供了一个可部署,不确定性意识的解决方案.
- 建议的贝叶斯增强导致制造业更可靠的自动化质量控制.
- 这项工作通过不确定性量化来提高自动化视觉检查系统的可靠性.
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