北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2022, Vol. 45 ›› Issue (5): 42-48.

• 论文 • 上一篇    下一篇

基于密集YOLOv3的印刷电路板缺陷识别

杨杰,张书杰   

  1. 东北大学 信息科学与工程学院
  • 收稿日期:2021-10-05 修回日期:2021-11-24 出版日期:2022-10-28 发布日期:2022-11-01
  • 通讯作者: 杨杰 E-mail:yangjie@ise.neu.edu.cn
  • 基金资助:
    国家重点研发计划项目; 国防预研项目

Defect Recognition of Printed Circuit Board Based on YOLOv3-Dense

YANG Jie,  ZHANG Shujie   

  • Received:2021-10-05 Revised:2021-11-24 Online:2022-10-28 Published:2022-11-01

摘要: 对印刷电路板加工过程中微小缺陷的精准检测是保证电子产品质量的前提由于电路板缺陷的特征尺寸极小,电路复杂,现有的目标检测方法存在很多不足针对这一问题,YOLOv3 算法的基础,提出了一种用于印刷电路板缺陷检测的密集 YOLOv3 目标检测算法首先,用密集连接卷积网络模块代替 YOLOv3 算法特征提取网络中的部分残差网络单元,增强网络的特征重用;其次,对损失函数加以改进,用预测框和真实值之间的广义交并比来解决交并比为零时无法继续优化的问题所提出的密集 YOLOv3 算法在扩充后的印刷电路板缺陷数据集上得到了有效地验证实验结果表明,与其他识别算法相比,所提算法在识别精度提高的同时,算法尺寸也有所减小

关键词: 微小目标检测, YOLOv3算法,  密集连接卷积网络,  印刷电路板缺陷

Abstract: Accurate detection of tiny defects in printed circuit board processing is the prerequisite to ensure the quality of electronic products. Due to the small feature size and complex circuit layouts, the existing target detection methods have many shortcomings. To solve this problem, a YOLOv3-dense target detection model is proposed for printed circuit board defect detection based on YOLOv3 algorithm. First, dense connection network modules are used to replace some residual units in the feature extraction network so as to enhance feature reuse of the network. Then, the loss function is improved, and the generalized intersection ratio between the prediction box and the true value is used to solve the problem that the optimization cannot continue when the intersection ratio is zero. The experimental results show that compared with other models, the proposed model can improve the recognition accuracy and reduce the model size.

Key words: small target detection,  YOLOv3 algorithm, densely connected convolutional networks, printed circuit board defects

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