[1]WU Ruilin,GE Quanbo,LIU Huaping.Class-incremental printed circuit board defect detection method based on YOLOX[J].CAAI Transactions on Intelligent Systems,2024,19(4):1061-1070.[doi:10.11992/tis.202309044]
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Class-incremental printed circuit board defect detection method based on YOLOX

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 1061-1070 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2024-07-05
Title:
Class-incremental printed circuit board defect detection method based on YOLOX
Author(s):
WU Ruilin1;  GE Quanbo1;  LIU Huaping2
1. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
deep learning; printed circuit board; class-incremental; incremental learning; defect detection; object detection; dynamic detection; knowledge distillation; catastrophic forgetting
CLC:
TP391
DOI:
10.11992/tis.202309044
Abstract:
To cope with more practical incremental printed circuit board detection scenarios, by combining the knowledge distillation with the YOLOX, this paper proposes a class-incremental Printed Circuit Board defect detection method based on YOLOX. The model can detect all learned defect types when only new training data is used. The transfer of knowledge about old defect categories is facilitated by using knowledge distillation for the model’s output features and intermediate features, enabling the student model to effectively retain the detection performance of the teacher model on old defect categories. The experimental results show that the method in this paper can significantly alleviate the catastrophic forgetting problem during the incremental learning process. Under the two-stage incremental scenario, the model has a mean average precision of 88.5% for all defects, a parameter size of 25.3 M, and an inspection speed of 39.8 f/s, which facilitates the deployment of industrial equipment and at the same time, it can satisfy the detection accuracy of printed circuit board (PCB) quality inspection and the inspection speed requirement in incremental detection scenarios.
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