[1]YANG Aofei,XU Xinying,XIE Gang,et al.Research on continual detection and localization method for printed circuit board defect[J].CAAI Transactions on Intelligent Systems,2025,20(1):219-229.[doi:10.11992/tis.202310024]
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Research on continual detection and localization method for printed circuit board defect

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 1 Page number: 219-229 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2025-01-05
Title:
Research on continual detection and localization method for printed circuit board defect
Author(s):
YANG Aofei1;  XU Xinying1;  XIE Gang1;  LIU Huaping2
1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
defect detection; defect localization; continual learning; deep learning; unsupervised learning; reverse distillation; one-class classification embedding; pooling distillation; printed circuit board
CLC:
TP391
DOI:
10.11992/tis.202310024
Abstract:
Existing defect detection and localization methods can only detect fixed types of defects and cannot meet the continual defect detection requirements in real application scenarios. To address this issue, this paper proposes a defect detection and localization method based on the reverse distillation model. This method uses the reverse distillation model as the basis model and performs pooling distillation on the feature maps from the middle layers of the model and the one-class classification embedding representation. So that the model can continually train new detection tasks without forgetting previous tasks. Experimental results on four printed circuit board defect detection and localization tasks show that this method can meet the requirements of industrial applications, and it outperforms other methods. It maintains the ability to learn and detect new tasks while suppressing the trend of forgetting the ability to detect samples of previous tasks.
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