Occluded Pedestrian Detection Algorithm Based on Improved Network Structure of YOLOv3

被引：0

作者：

Liu L. ^{[1
,2
]}

Zheng Y. ^{[1
,2
,3
]}

Fu D. ^{[1
,2
]}

机构：

[1] School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing

[2] Beijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology Beijing, Beijing

[3] Shunde Graduate School, University of Science and Technology Beijing, Foshan

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 06期

关键词：

Deep Learning; Network Pruning; Pedestrian Detection; Spatial Pyramid Pooling Network; YOLOv3;

D O I：

10.16451/j.cnki.issn1003-6059.202006010

中图分类号：

学科分类号：

摘要：

Aiming at high missed detection rates of YOLOv3 for occluded pedestrian in surveillance video, a detection method for occluded pedestrian based on improved network structure of YOLOv3 is proposed. Firstly, the spatial pyramid pooling network is introduced into the fully connected layer to enhance the multi-scale feature fusion capability of the network. Secondly, the network structure pruning is employed to eliminate the network structure redundancy to avoid network degeneration and overfitting problem caused by the deepening of network layers and reduce the amount of parameters. Multi-scale training is performed on the corridor pedestrian dataset to obtain the best weight model. Experimental results indicate the improvement of average accuracy and detection speed of the proposed algorithm. © 2020, Science Press. All right reserved.

引用

页码：568 / 574

页数：6

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