Human Object Detection Algorithm Based on Improved YOLOv3
LIANG Zhi
WU Tao
XUE Yansong
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.
Keywords：YOLOv3human object detectionLR-Darknet41 networkfeature fusionRFB-s
Publication Date：2024-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 3503-3508,3554 )