RSD-YOLOv8n: A Lightweight PCB Defect Detection Algorithm

Source abstract

In industrial PCB defect detection, the current object detection algorithm suffers from low detection accuracy and difficulties in deployment on edge detection devices. The RSD-YOLOv8n algorithm was proposed to optimize accuracy, speed, and compactness for PCB defect detection. In actual design, the C2f layer in the feature extraction network is improved by employing feature reuse and structural reparameterization techniques, and the enhanced C2f-RepGhost module is introduced to strengthen the feature extraction capability of the backbone network. And the SPDConv module was adopted. This module converts spatial information in the feature maps into depth information while maintaining the resolution of the feature maps, further enhancing the feature extraction capabilities. Meanwhile, the C2f-DWR module is used. By partitioning the input feature maps and fusing feature information across different scales, it enhances the network to integrate feature maps of varying scales. Ablation and comparative experiments show that the RSD-YOLOv8n algorithm increases mAP@50 from 93.4% to 95.9% compared to the baseline model, while also reducing the number of network parameters. Generalization experiments demonstrate that this RSD-YOLOv8n algorithm has high generalization ability. Our method provides a solution for PCB defect detection.