To solve the problems of low accuracy,slow speed and large number of model parameters in the current Printed Circuit Board(PCB)defect detection network,a Lightweight Weighting Novel Network(LWN-Net)based on improved YOLOv3 was proposed.To solve the excessive number of backbone network(Darknet53)parameters in YOLOv3,a lightweight feature augmentation network was proposed as feature extraction network for the model.Considering that the detection accuracy would be reduced caused by imbalance of semantic information and location information in the process of feature extraction,the weight aggregation distribution mechanism was introduced to e-liminate imbalance and improve the feature extraction ability of the model.A novel feature pyramid network was proposed to enhance the network's ability to extract detailed information and reduce information redundancy.To speed up the convergence of the model and improve the detection accuracy,the regression loss function SIoU was added to the network training.The result showed that the model size was compressed by 87.5％by comparing with YOLOv3,but the detection speed was increased by 8.32 frames,the prediction accuracy and recall rate were in-creased by 0.88％and 1.6％.The proposed network provided a more efficient method for PCB defect detection problem.
printed circuit board defect detectionYOLOv3lightweightSIoU loss function
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