Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 240400192-9.doi: 10.11896/jsjkx.240400192

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Object Detection-based Method for Guiding Passenger Flow in Boarding and Deparking Areas ofRail Transit

LE Lingzhi1,2, ZHAI Jiangtao2, YU Ming1, SUN Tongqing2   

  1. 1 Nanjing Guodian Nanzi Rail Transit Engineering Co.,Ltd.,Nanjing 210000,China
    2 School of Electronics and Information Engineering,University of Information Science & Technology,Nanjing 210044,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:LE Lingzhi,born in 1976,senior engineer.His main research interests include smart urban rail transit and intelligent substation.
  • Supported by:
    National Natural Science Foundation of China(U21B2003) and Industry Outlook and Key Core Technology Projects in Jiangsu Province(BE2022075).

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Abstract: To address the situation where passengers occupy the alighting area while waiting in front of the platform screen doors,this paper proposes a passenger flow guidance method based on object detection.Firstly,an improved MCA-YOLOv5s network model is proposed by enhancing the shape features of passengers in front of the platform screen doors for object detection.Then the field of view angle and installation angle of the camera are calculated based on the mounting height of the intelligent door lintel system and the range of the alighting area in real scenes to ensure accurate division of the alighting and boarding areas in captured images.Subsequently,passenger density estimation is conducted for the alighting and boarding areas,and correspon-ding passenger flow distribution strategies are designed based on the estimated density values,with guidance provided through speakers on the intelligent door lintel terminal.Through testing in real scenarios,the effectiveness of this method in rapidly and accurately estimating passenger density is validated.

Key words: Object detection, Passenger density, Passenger flow distribution

CLC Number: 

  • TP391
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