Laser & Optoelectronics Progress, Volume. 62, Issue 12, 1215008(2025)

Efficient Printed Circuit Board Defect Segmentation Technology Based on YOLO Prompts and ICT-ViT

Tieqiang Sun1... Hongjian Yu1,*, Can Zhang1, Yidong Yuan1 and Aoran Sun2 |Show fewer author(s)
Author Affiliations
  • 1College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, Hebei , China
  • 2School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu , China
  • show less

    The complete outline of printed circuit board (PCB) defects is difficult to define and is easily affected by the background of the board, resulting in difficulty in separating the shape and size of image defects. Therefore, this study proposes a method based on improved MobileSAMv2 to efficiently extract the defect morphology in defective boards. First, the YOLO object detection technology is introduced to provide accurate mask information of the model, solve the ambiguity problem, and then optimize the segmentation performance. Second, feature fusion technology is used to construct a feature converter network Vision Transformer (ViT) called ICT-ViT, which fuses the inputs of local convolutional neural network and global ViT and adapts to the characteristics of hardware acceleration by sacrificing part of the parameters in exchange for the improvement of overall performance. Finally, the decoding speed and accuracy are further improved by fine-tuning the parameters of the mask decoder. The experimental results show that the accuracy of the model decreases obviously when the tuning interval exceeds 40%. On the PKU-Market-PCB dataset, the optimized model achieves millisecond-level inference speed while maintaining an mean intersection over union of 0.976 and average recall score (mScore) of 0.889. In addition, it shows good performance in small-target defect contour segmentation, which not only meets the need for high efficiency, but also ensures the accuracy of the processing results.

    Keywords
    Tools

    Get Citation

    Copy Citation Text

    Tieqiang Sun, Hongjian Yu, Can Zhang, Yidong Yuan, Aoran Sun. Efficient Printed Circuit Board Defect Segmentation Technology Based on YOLO Prompts and ICT-ViT[J]. Laser & Optoelectronics Progress, 2025, 62(12): 1215008

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Machine Vision

    Received: Nov. 27, 2024

    Accepted: Jan. 2, 2025

    Published Online: Jun. 12, 2025

    The Author Email: Yu Hongjian (yhjadd@163.com)

    DOI:10.3788/LOP242339

    CSTR:32186.14.LOP242339

    Topics