基于改进YOLOv5s模型的客流密度分析系统设计与实现

Passenger flow density analysis system based on improved YOLOv5s model

客流密度分析是地铁运营管理、保障乘客安全、构建客流大数据平台的重要基础。针对运营方提出车站、列车车厢客流密度管理的需求,设计了基于改进YOLOv5s模型的客流密度分析系统。该系统通过改进YOLOv5s目标检测模型,引入注意力机制、改进主干网络结构,保持模型轻量化的同时提高人群检测精准度和推理速度。基于北京新机场线乘客信息系统项目测试应用表明,该系统识别速度快、分析精度高,有利于地铁运营对客流的全面监管。

Passenger density analysis is an important foundation for subway operation management,ensuring passenger safety,and building a passenger flow big data platform.This paper designed a passenger flow density analysis system based on the improved YOLOv5s model in response to the operator's demand for station and train carriage passenger flow density management.The system improved the YOLOv5s object detection model,introduced attention mechanism,and improved the backbone network structure to maintain model lightweight while improving crowd detection accuracy and inference speed.The test application of the passenger information system project based on the Beijing New Airport Line shows that the system has fast recognition speed and high analysis accuracy,which is conducive to the comprehensive supervision of passenger flow by subway operations.