A real-time PCB defect detection model based on enhanced semantic information fusion.

Autor: Ji, Tangyu, Zhao, Qian, An, Kang, Liu, Dandan, Yu, Wentao, Liang, Shuang
Zdroj: Signal, Image & Video Processing; Aug2024, Vol. 18 Issue 6/7, p4945-4959, 15p
Abstrakt: Aiming at the problem of poor balance between real-time and performance of existing PCB board defect detection algorithms, this paper proposes a fast PCB board detection model based on enhanced semantic information fusion, i.e., the Ghost-YOLOv8 (G-YOLOv8) model. GhostConv is used for feature extraction in the backbone network part, which reduces the complexity of network operation; the SPPFCSPCS structure is proposed and applied to the deepest layer of the backbone network to strengthen the model's fusion ability for deep multi-scale semantic information. In addition, after the feature fusion large-scale detection head, the low parameter number A 2 attention mechanism is added to improve the model's attention to high-dimensional valid semantic information and optimize the detection results of the network; the Wise-IoU loss function is used for iteration in the training process to strengthen the model's fitting ability. The experimental results show that the G-YOLOv8 model can detect up to 125FPS on the Peking University open-source PCB board defect detection dataset and Deep PCB dataset, which is an improvement of 1.5FPS compared to the pre-improvement period; and the mAP0.5 on the deep PCB dataset improves by 0.8%. The performance and detection speed balance of the network has been improved. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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