在RT-DETR上通过HSA-RTDETR进行增强的PCB缺陷检测
Yesong Wang1,2, Binbin Wu1, Lihua Zhang3
1School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
Scientific reports
|August 28, 2025
概括
一个新的等级级注意力 (HSA) 机制,HSA-RTDETR,增强了印刷电路板 (PCB) 的缺陷检测. 这种人工智能驱动的方法显著提高了识别小缺陷的准确性和速度,促进了制造业的自动化检查.
科学领域:
- 计算机视觉和人工智能
- 制造业和工业自动化
- 电子工程和质量控制
背景情况:
- 印刷电路板 (PCB) 的缺陷,如缺少的孔和短线,可能会导致产品故障.
- 自动化PCB检查对于智能制造和工业4.0至关重要,但面临着高密度,微型元件和复杂背景的挑战.
- 现有的机器视觉和人工智能算法难以检测多层次的缺陷,
研究的目的:
- 提出基于人工智能的先进方法,用于自动化和智能化的PCB缺陷检测.
- 提高PCB中小型和复杂缺陷的检测.
- 提高PCB缺陷检查系统的效率和准确性.
主要方法:
- 在RT-DETR (实时检测变压器) 框架中开发了一种新的层次意识注意力 (HSA) 机制,创建了HSA-RTDETR模型.
- 引入了一个新的骨干网络 (R18-Faster-EMA),以提高模型的效率.
- 重新设计基于注意力的尺度内特征交互 (AIFI) 模块,使用级联群体注意力,并实施分层尺度感知金字塔注意力网络 (HS-PAN) 来实现多尺度特征融合.
- 设计了一个新的损失函数来加速融合,并优先检测小缺陷.
主要成果:
- 在PCB缺陷数据集上,HSA-RTDETR方法实现了96.9%的平均精度 (mAP),性能优于现有模型.
- 与原来的RT-DETR显著改进:精度增加5.8%,回忆增加7.9%,mAP50提高5.4%.
- 实现了每秒 66.2 的高推断速度,证明了其实时检测小型 PCB 缺陷的有效性.
结论:
- 拟议的HSA-RTDETR方法为PCB缺陷的自动检测提供了一个非常有效的解决方案,特别是对于小而具有挑战性的缺陷.
- 集成层次意识的注意力和高效的网络设计提高了检测的准确性和速度.
- 这种方法代表了智能制造环境中质量控制的重大进步.
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