Laser & Optoelectronics Progress, Volume. 62, Issue 12, 1215008(2025)
Efficient Printed Circuit Board Defect Segmentation Technology Based on YOLO Prompts and ICT-ViT
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.
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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
Category: Machine Vision
Received: Nov. 27, 2024
Accepted: Jan. 2, 2025
Published Online: Jun. 12, 2025
The Author Email: Yu Hongjian (yhjadd@163.com)
CSTR:32186.14.LOP242339