Research on PCB defect detection algorithm based on LPCB-YOLO - Zhang, Haiyan; Li, Yazhou; Sharid Kayes, Dipu Md; Song, Zhaoyu; Wang, Yuanyuan (Frontiers in Physics, 2025)
IntroductionIn response to the challenges of small target size, slow detection speed, and large model parameters in PCB surface defect detection, LPCB-YOLO was designed. The goal was to ensure detection accuracy and comprehensiveness while significantly reducing model parameters and improving computational speed.MethodFirst, the feature extraction networks consist of multiple CSPELAN modules for feature extraction of small target defects on PCBs. This allows for sufficient feature representation while greatly reducing the number of model parameters. Second, the C-SPPF module enables the fusion of high-level semantic expression with low-level feature layers to enhance global feature perception capability, improving the overall contextual expression of the backbone and thereby enhancing model performance. Finally, the C2f-GS module is designed to fuse high-level semantic features and low-level detail features to enhance the feature representation capability and model performance.ResultsThe experimental results show that the LPCB-YOLO model reduces the model size by 24% compared to that of the YOLOv8 model while maintaining high precision and recall at 97.0%.