摘要
Abstract
In the driving scenario,due to the occlusion between pedestrians and their scale varia-tions,detection model have low accuracy,high model parameters,and difficulty in deploying to mobile terminals.This paper proposes a lightweight real-time pedestrian detection model,LPD-YOLO,based on the YOLOv5s model.Firstly,in the feature extraction part,the original backbone network is re-placed with MES Net(Mish-Enhanced Shuffle Net),and an attention module SA(Shuffle Attention)is embedded in the backbone network to enhance network feature extraction ability.Secondly,in the fea-ture fusion part,the original PANet is improved by using the DS-ASFF structure to fully fuse feature maps of different sizes.Then,standard convolution is replaced with GS convolution in the feature-covergent network part without affecting accuracy,further reducing model parameters and computation.Finally,in the prediction part,the original loss function is improved by using the OTA label assignment strategy combined with α-IOU to accelerate model convergence.Experimental data show that compared with YOLOv5s,LPD-YOLO has 81.2%fewer parameters,46.3%lower floating-point operation volume,75.8%smaller model size,and 3.3%higher detection accuracy.The single image detection speed is 13.2 ms,which better meets the real-time detection requirements of dense pedestrians in driv-ing scenarios.关键词
行人检测/轻量级网络/YOLOv5s/注意力机制Key words
pedestrian detection/lightweight network/YOLOv5s/attention mechanism分类
信息技术与安全科学
