Lightweight Pedestrian Detection Algorithm Based on YOLOv5s Fused Attention Mechanism
The pedestrian detection technology based on YOLOv5s algorithm has been widely ap-plied in autonomous driving.Lightweight improvements on YOLOv5s algorithm can be reduced compu-tational resources,storage space,and transmission bandwidth.This work is of great practical signifi-cance.To enhance the model's attention to key features,a CBAM attention mechanism can be fused into the backbone network to suppress irrelevant information.Simultaneously,to reduce model complexity,parameter count,and computational requirements,a Ghost structure of GhostNet network is incorpo-rated to replace the original convolutional of YOLOv5s and C3 structure of Neck module.To verify the advantages of the lightweight algorithm,based on PASCAL VOC 2007 dataset and WiderPerson data-set,the original YOLOv5s and the improved algorithm are tested.The results demonstrate that the lightweight algorithm can greatly reduce parameter count and computational requirements while maintai-ning the detection and recognition accuracy of the original YOLOv5s algorithm.
YOLOv5pedestrian detectionattention mechanismlightweight model
万方数据
维普
