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ASTKD-PCB-LDD: high-performance PCB defect detection model with align soft-target knowledge distillation and lightweight network design

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Abstract

Defects in printed circuit boards (PCBs) can degrade the performance and reliability of electronic devices. Although YOLOv5-based algorithms are commonly used to detect PCB defects, their complex parameters slow down detection speeds on industrial platforms. This paper presents a lightweight, high-performance model for PCB defect detection, called Align Soft-Target Knowledge Distillation PCB Lightweight Defect Detection (ASTKD-PCB-LDD). The model uses the k-means++ algorithm for optimal anchor box selection and the SCYLLA-IoU (SIoU) loss function to improve accuracy in detecting small defects. The Faster-Ghost backbone network and slim-neck architecture reduce computational load and improve inference speed. Additionally, Align Soft-Target Knowledge Distillation (ASTKD) is applied, with the PCB-LDD model as the teacher and a pruned model-created using Layer-Adaptive Magnitude-based Pruning (LAMP)-as the student. This strategy helps to maintain detection accuracy while reducing model size. Experimental results show that the model size is reduced from 14.5 to 4 MB (a 27.6\(\%\) reduction), achieving 98\(\%\) mean average precision (mAP), and the detection speed increases from 73.2 frames/s to 112.3 frames/s, improving by 153.4\(\%\). Moreover, the model demonstrates strong applicability and scalability. This approach effectively combines performance and lightweight design, significantly enhancing PCB defect detection efficiency.

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Funding

This work was supported by the Key Projects for Postgraduate Students of Hunan Provincial Department of Education (No. CX20210742).

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Authors and Affiliations

  1. School of Physics and Electronic Science, Changsha University of Science and Technology, 960th Wanjiali Street, Changsha, 410114, Hunan, China

    Zhelun Hu, Zhao Zhang, Shenbo Liu, Dongxue Zhao, Longhao Zheng & Lijun Tang

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  1. Zhelun Hu
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  2. Zhao Zhang
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  3. Shenbo Liu
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Correspondence to Lijun Tang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Preprint declaration

An earlier version of this work, titled "ASTKD-PCB-LDD: A Lightweight High Performance PCB Defect Detection Algorithm," was published as a preprint on Research Square (DOI: https://doi.org/10.21203/rs.3.rs-4008736/v1). This manuscript includes important revisions and additional experimental results that were not part of the preprint.

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Hu, Z., Zhang, Z., Liu, S. et al. ASTKD-PCB-LDD: high-performance PCB defect detection model with align soft-target knowledge distillation and lightweight network design. J Supercomput 81, 531 (2025). https://doi.org/10.1007/s11227-025-07045-9

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