用多个模型集成提升电路板缺陷检测准确率
Enhancing Printed Circuit Board Defect Detection through Ensemble Learning
- 融合四种主流目标检测模型构建集成框架
- 在多种缺陷上达到95%检测准确率
- 适合需要高可靠性的电子制造质检场景
印刷电路板（PCB）的质量控制对电子设备技术发展至关重要。尽管已有众多机器学习方法用于提升缺陷检测的效率与准确率，但以往研究多聚焦于单一模型对特定缺陷类型的优化，忽略了不同方法间的协同潜力。本文提出一种综合检测框架，采用集成学习策略弥补这一不足。首先，选用四种基于前沿技术的PCB缺陷检测模型：EfficientDet、MobileNet SSDv2、Faster RCNN和YOLOv5，各自独立识别缺陷。随后，将这些模型整合进一个集成学习框架中以提升整体性能。对比分析显示，该集成框架显著优于各单独模型，在多种缺陷检测任务中达到95%的准确率。结果表明，所提出的集成学习框架能有效提升PCB质量控制水平。
原文摘要 · Abstract (English)
The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-the-art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes.
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