计算机与数字工程2024，Vol.52Issue(12)：3503-3508,3554,7.DOI:10.3969/j.issn.1672-9722.2024.12.005
基于改进YOLOv3的人体目标检测算法研究
Human Object Detection Algorithm Based on Improved YOLOv3
摘要
Abstract
There are more problems that YOLOv3 have low detection accuracy,high missed detection rate for small target ob-jects in human detection,and YOLOv3's detection speed cannot meet the needs of real-time detection scene.To deal with these prob-lems,this paper proposes a human object detection algorithm based on improved YOLOv3.Firstly,the K-means++algorithm is uti-lized to cluster the target boundaries in the VOC data set of the person,and the priori parameters of the network are optimized by the clustering results.Secondly,the algorithm acquires a lightweight LR-Darknet41 by pruning the backbone network structure of Dark-net53,which can decrease the parameters of the model and improve the detection speed.Finally,the fusion of the shallow and deep features is achieved by using RFB-s,which can expand the receptive field and augment the detection of small-scale human target.The data show that,compared with the original algorithm,the improved algorithm reduces the missed detection rate by 3.7%and in-creases the detection accuracy by 4.1%,and the detection speed reaches 53.6 frames/s.关键词
YOLOv3/人体检测/LR-Darknet41网络/特征融合/RFB-sKey words
YOLOv3/human object detection/LR-Darknet41 network/feature fusion/RFB-s分类
信息技术与安全科学引用本文复制引用
梁芷,毋涛,薛岩松..基于改进YOLOv3的人体目标检测算法研究[J].计算机与数字工程,2024,52(12):3503-3508,3554,7.基金项目
陕西省科技成果转移与推广计划(编号:2019CGXNG-018)资助. （编号:2019CGXNG-018）
