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Lightweight intelligent detection algorithm for surface defects in printed circuit board
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AI
DOI：10.1016/j.cie.2025.111030.png)
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Abstract
En 中文
Printed circuit boards (PCBs) are the core components of electronic devices, and deep learning-based image recognition technology effectively diagnoses defects, ensuring product quality and reliability. To address the challenges of small defect detection and model lightweighting, this paper introduces a lightweight PCB surface defect detection model (PSDDNet). Firstly, a multi-branch streaming convolution (MSC) is designed to aggregate features through continuous convolution and pooling, capturing rich gradient flow information to improve the receptive field and feature representation capabilities. Secondly, a simplified GDLite architecture is designed for feature fusion, utilizing the gather-distribute mechanism to optimize cross-layer information interaction, thereby avoiding feature information loss and confusion, and reducing model complexity. Additionally, the feature extraction architecture is optimized to better focus on small target information, and a lightweight coordinate attention (CA) module is introduced to enhance feature expression capabilities. Extensive experiments on three PCB datasets demonstrate the superiority of PSDDNet, showing a better balance of detection precision and speed compared to other state-of-the-art algorithms. On the PKUMarket-PCB dataset, PSDDNet achieves an inference speed of 65 FPS, while obtaining 98% mAP and 97.7% recall with only 0.9 million parameters. These experiments prove that PSDDNet is a reliable and competitive model, providing a feasible solution for real-time PCB defect detection in industrial applications.
Keywords:
Circuit board
Defect detection
Multi-branch streaming convolution
Gather-distribute mechanism
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