吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (4): 1258-1265.doi: 10.13229/j.cnki.jdxbgxb.20240756
基于改进YOLO算法的地铁车厢客流检测方法
郭宁1,2(
),胡小晨1,2,董德存1()
- 1.同济大学 道路与交通工程教育部重点实验室，上海 201804
2.同济大学 上海市轨道交通结构耐久与系统安全重点实验室，上海 201804
Passenger flow detection method of subway car based on improved YOLO algorithm
Ning GUO1,2(),Xiao-chen HU1,2,De-cun DONG1()
- 1.Key Laboratory of Road and Traffic Engineering，Ministry of Education，Tongji University，Shanghai 201804，China
2.Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety，Tongji University，Shanghai 201804，China
摘要：
在高峰时段，地铁车厢的客流量剧增，人群密集和目标遮挡等复杂场景导致传统方法难以准确识别每一个乘客，容易导致漏检或误检。针对此问题，提出了基于改进YOLO算法的地铁车厢客流检测方法。分析YOLOv8模型结构后，将ASF-YOLO中的TFE模块加入YOLOv8n中，结合时空模型，考虑到站时乘客流动大和行驶时乘客活动少的特点，及车门和车厢内乘客的不同流动特性，将多帧检测结果相融合，实现了对地铁车厢内乘客流量的精准检测。经实验比较：原始YOLOv8n模型平均精度为57.0%，改进后的模型为69.1%，多帧融合处理后为76.6%。基于该模型所得到的客流信息可用于乘客出行引导、应急救援、铁路运营管控等场景。
中图分类号:
- U293
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