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简单有效的PCB表面缺陷检测方法

Du Lang1, Zhenzhen Lv2

  • 1School of Information and Design, Zhejiang Industry Polytechnic College, Shaoxing, 312000, China. 20230037@zjipc.edu.cn.

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|March 29, 2025
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概括
此摘要是机器生成的。

一个新的算法,SEPDNet (简单有效的PCB缺陷检测网络),提供高效和准确的印刷电路板 (PCB) 缺陷检测. 这种定制网络在比现有模型更少的参数下实现了卓越的性能.

关键词:
缺陷检测 检测缺陷检测 检测缺陷检测在PCB中出现的缺陷印刷电路板 印刷电路板小物体检测 小物体检测

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科学领域:

  • 计算机视觉 计算机视觉
  • 制造业自动化 制造业自动化
  • 人工智能的人工智能

背景情况:

  • 手动检查印刷电路板 (PCB) 是耗时且昂贵的.
  • 现有的自动缺陷检测方法通常使用YOLO和Faster R-CNN等模型.
  • 一个重大挑战在于检测PCB数据集中的小目标和集中大小分布.

研究的目的:

  • 开发一个高效准确的缺陷检测算法,适用于具有特定特征的PCB数据集.
  • 设计一个简化的网络架构,减少参数数量,同时提高检测性能.
  • 为了证明基于数据集属性的定制模型设计的有效性.

主要方法:

  • 设计了一个新的网络,SEPDNet (简单有效的PCB缺陷检测网络).
  • SEPDNet使用单个检测头来最大限度地减少参数并提高性能.
  • 在脊柱中使用RepConv (可重新参数化的卷积) 进行增强的表示,在部使用FPN (特征金字塔网络) 进行模型简化.

主要成果:

  • 与YOLOv9u-s相比,SEPDNet实现了优越的性能,尽管其参数不到30%.
  • 在F1得分 (+0.025),mAP50 (+2.7%) 和mAP50:95 (+3.8%) 中也出现了改善.
  • 定制化方法在简化结构下表现出强的性能.

结论:

  • 根据数据集特征定制模型设计是改善PCB缺陷检测的可行策略.
  • 对于高效准确的自动化PCB检查,SEPDNet提供了一个有前途的解决方案.
  • 这项研究强调了定制深度学习模型在工业应用中的潜力.