Human Object Detection Algorithm Based on Improved YOLOv3
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.
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