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An adaptive feature reconstruction network for the precise segmentation of surface defects on printed circuit boards

Author

Listed:
  • Danqing Kang

    (Sun Yat-Sen University)

  • Jianhuang Lai

    (Sun Yat-Sen University
    Guangzhou Xinhua University)

  • Junyong Zhu

    (Intelligence Eyes Co., Ltd)

  • Yu Han

    (Sun Yat-Sen University
    Guangdong Provincial Key Laboratory of Fire Science and Technology)

Abstract

Segmentation networks based on deep learning are widely used in the field of industrial vision inspection, including for the precise segmentation of surface defects on printed circuit boards (PCBs). However, most previous studies have focused only on the utilization of defect samples with visible defects and underestimated the value of template samples without surface defects. In fact, template samples can provide sufficient prior information to identify defects and are not difficult to obtain in many manufacturing scenarios. Therefore, an adaptive feature reconstruction network (AFRNet) is proposed in this paper to utilize these two types of samples. Specifically, AFRNet consists of two main components: a Siamese encoder with shared parameters for extracting features from the input sample pair, and a symmetrical feature reconstruction module for adaptively fusing these extracted features. Similar image-level and feature-level fusion schemes, as well as spatial misalignment caused by unaligned sample pairs have been carefully studied. Extensive experiments on a real-world PCB surface-defect dataset confirm the effectiveness of the proposed method, demonstrating that it can significantly improve the segmentation performance of multiple baselines, such as DANet, PSPNet and DeepLabv3.

Suggested Citation

  • Danqing Kang & Jianhuang Lai & Junyong Zhu & Yu Han, 2023. "An adaptive feature reconstruction network for the precise segmentation of surface defects on printed circuit boards," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3197-3214, October.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:7:d:10.1007_s10845-022-02008-w
    DOI: 10.1007/s10845-022-02008-w
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    References listed on IDEAS

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    1. Ruizhen Liu & Zhiyi Sun & Anhong Wang & Kai Yang & Yin Wang & Qianlai Sun, 2020. "Real-time defect detection network for polarizer based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1813-1823, December.
    2. Haiyong Chen & Yue Pang & Qidi Hu & Kun Liu, 2020. "Solar cell surface defect inspection based on multispectral convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 453-468, February.
    3. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
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