Metro Pedestrian Detection Algorithm Based on Multi-scale Weighted Feature Fusion Network

Xiaowei DONG; Yue HAN; Zheng ZHANG; Hongbin QU; Guofei GAO; Mingdian CHEN; Bo LI

doi:10.11999/JEIT200450

Volume 43 Issue 7

Jul. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(7): 2113-2120

Xiaowei DONG, Yue HAN, Zheng ZHANG, Hongbin QU, Guofei GAO, Mingdian CHEN, Bo LI. Metro Pedestrian Detection Algorithm Based on Multi-scale Weighted Feature Fusion Network[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2113-2120. doi: 10.11999/JEIT200450

Citation:

Xiaowei DONG, Yue HAN, Zheng ZHANG, Hongbin QU, Guofei GAO, Mingdian CHEN, Bo LI. Metro Pedestrian Detection Algorithm Based on Multi-scale Weighted Feature Fusion Network[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2113-2120. doi: 10.11999/JEIT200450

Citation:

Xiaowei DONG, Yue HAN, Zheng ZHANG, Hongbin QU, Guofei GAO, Mingdian CHEN, Bo LI. Metro Pedestrian Detection Algorithm Based on Multi-scale Weighted Feature Fusion Network[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2113-2120. doi: 10.11999/JEIT200450

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Metro Pedestrian Detection Algorithm Based on Multi-scale Weighted Feature Fusion Network

doi: 10.11999/JEIT200450 cstr: 32379.14.JEIT200450

1.
School of Information Science and Technology, North China University of Technology, Beijing 100144, China
2.
International Business Department, China Petroleum Pipeline Engineering Co., Ltd., Beijing 065000, China
3.
Beijing Urban Construction Design and Development Group Co., Ltd., National Engineering Laboratory for Green and Safe Construction Technology of Urban Rail Transit, Beijing 100037, China

Funds: Beijing Natural Science Foundation (4192002), The Scientific Research Foundation of North University of Technology

Received Date: 2020-06-02
Rev Recd Date: 2020-10-18

Available Online: 2020-10-21

Publish Date: 2021-07-10

Abstract

Abstract

With the large increase of passengers in metro stations, precise and real-time monitoring of passenger flow in subway stations is of great significance for ensuring passenger safety. Based on the features of complicated subway scenes and small pedestrian targets, a Multi-scale Weighted Feature (MWF) fusion network to achieve accurate real-time monitoring of subway passengers is proposed. In the data preprocessing stage, an oversampling target enhancement algorithm is proposed to stitch the pictures with an insufficient proportion of small targets to increase the iteration frequency of small targets during training. Secondly, feature extraction layers based on the VGG16 network are added to the Single Shot multibox Detector (SSD) network. The feature layers of different scales are weighted and fused in different ways, and the optimal feature fusion method is selected. Finally, combined with the small target oversampling enhancement algorithm, a multi-scale weighted feature fusion model is obtained. Experiments show that the detection accuracy of this method has improved by 5.82 percent compared with the SSD network and doesn’t reduce the speed of detection.
- Target detection,
- Small target,
- Deep network,
- Weighted feature fusion

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References(21)

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