Improved YOLOv5s Component Detection Technology for PCB
This paper proposes a lightweight PCB component target detection YOLO-SCGS based on YOLOV5s to address the high hardware computational requirements and slow detection speed in PCB com-ponent detection technology.This algorithm replaces the backbone network with ShuffleNetv2,introduces CA attention mechanism in the Neck layer,and designs a cross level partial network GSCSP module for lightweight standard convolution.Finally,SPD Conv is used to reconstruct the CNN architecture,replacing the convolution step size and convolution layer to improve detection accuracy.The experimental results show that compared to the original YOLOv5s,the algorithm proposed in this paper improves detection speed by 16.61％,reduces floating-point computing requirements by 85.44％,and reduces model weight by 84.83％.It has lower computational requirements and better detection speed,meeting the requirements of lightweight local deployment.
万方数据
维普
