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DSRF: few-shot PCB surface defect detection via dynamic selective regulation fusion

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Abstract

Defect detection in printed circuit board (PCB) is critical to quality control in their production process. Conventional detection methods rely on a large number of annotated images, while the acquisition of defect samples is time-consuming and labor-intensive. Few-shot object detection detects novel classes with a few instances and attracts an increased interest. We propose a dynamic selective regulation fusion method (DSRF) for few-shot PCB surface defect detection. Concretely, we design a selective feature enhancement (SFE) module that focuses on foreground information while suppressing irrelevant background details, enabling more effective utilization of useful information in defective samples. Additionally, we introduce a channel regulation transformation (CRT) module that enhances the detection capability of the network by constructing inter-channel relationships and capturing key information about tiny defects. In order to overcome the shortcomings of existing meta-learning methods in query image perception, we introduce a dynamic information fusion (DIF) module to effectively integrate the information in the query branch into the support branch, and to improve the feature expression ability of the support features. Experiments on the PCB datasets demonstrate that our method significantly outperforms state-of-the-art baselines across different sample settings. Our method not only achieves significant improvement in accuracy, but also demonstrates superiority in handling few-shot data, providing an effective solution for PCB defect detection. Code is available at https://github.com/lydcv/DSRF.

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Data availability

All data generated or analyzed in this study are included in this paper. Code availability: Our code is available at https://github.com/lydcv/DSRF.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, Shandong, China

    Yudong Li, Shaoqing Wang, Zihao Jing, Xiao Zheng & Fuzhen Sun

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, 211112, Jiangsu, China

    Jinghua Zheng

  3. Inner Mongolia Institute of Dynamical Machinery, Hohehot, Inner Mongolia, 010010, China

    Xiaobo Han

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  1. Yudong Li
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  4. Jinghua Zheng
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Contributions

All authors contributed to the conception and design of the study. Li Yudong, Wang Shaoqing and Jing Zihao organized the data and designed the model. The first draft of the manuscript was written by Li Yudong, and all authors commented on previous version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shaoqing Wang.

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Li, Y., Wang, S., Jing, Z. et al. DSRF: few-shot PCB surface defect detection via dynamic selective regulation fusion. J Supercomput 81, 529 (2025). https://doi.org/10.1007/s11227-025-07071-7

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