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Surface Defect Detection of Electronic Components Based on FaSB R-CNN

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Cognitive Systems and Information Processing (ICCSIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1787))

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Abstract

In printed circuit board (PCB) defect detection, there are some imbalances, which generally consists in feature level and sample level. The feature imbalance problem is caused by the semantic loss of features in the feature maps. The sample imbalance is caused by two aspects, defects of different scales and the incomplete utilization of negative sample information. These can affect the accuracy of defect detection. Resolving these imbalances is an important research direction to improve the accuracy of detection. To alleviate the impact of imbalance, a sample and feature equalization-based R-CNN detection algorithm (Feature and Simple Balance R-CNN, FaSB R-CNN) is proposed. It contains three innovative components: Feature Balance Processing (Feature Balance Algorithm and FPN), GA-RPN and IoU-Balanced Sampling. Using the above three components in the basic Faster R-CNN system, our method (FaSB R-CNN) has a large improvement in PCB defect detection. FaSB R-CNN achieves 8.18% higher precision on PCB Data set than the Faster R-CNN. Compared with other object detection algorithms, the FaSB R-CNN can also achieve relatively good results and has good practical engineering application value.

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Acknowledgement

This work is partially supported by the Research Project Supported by Shanxi Scholarship Council of China (No. 2021-046), Natural Science Foundation of Shanxi Province (No. 202103021224056) and Shanxi Science and Technology Cooperation and Exchange Project (No. 202104041101030).

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Authors and Affiliations

  1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, China

    Zihao Zheng, Wenjing Zhao, Haodong Wang & Xinying Xu

Authors
  1. Zihao Zheng
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  2. Wenjing Zhao
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  3. Haodong Wang
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  4. Xinying Xu
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Corresponding author

Correspondence to Xinying Xu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. University of Manchester, Manchester, UK

    Angelo Cangelosi

  3. Universität Hamburg, Hamburg, Germany

    Jianwei Zhang

  4. Fuzhou University, Fuzhou, China

    Yuanlong Yu

  5. Tsinghua University, Beijing, China

    Huaping Liu

  6. Tsinghua University, Beijing, China

    Bin Fang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zheng, Z., Zhao, W., Wang, H., Xu, X. (2023). Surface Defect Detection of Electronic Components Based on FaSB R-CNN. In: Sun, F., Cangelosi, A., Zhang, J., Yu, Y., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2022. Communications in Computer and Information Science, vol 1787. Springer, Singapore. https://doi.org/10.1007/978-981-99-0617-8_40

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  • DOI: https://doi.org/10.1007/978-981-99-0617-8_40

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0616-1

  • Online ISBN: 978-981-99-0617-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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