| 基于改进YOLOv5s的列车车厢客流密度检测方法研究 |
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| 引用本文： | 张馨,董承梁,汪晓臣,田源.基于改进YOLOv5s的列车车厢客流密度检测方法研究[J].铁路计算机应用,2022,31(10):10-15. |
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| 作者姓名： | 张馨 董承梁 汪晓臣 田源 |
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| 作者单位： | 1.中国铁道科学研究院集团有限公司 电子计算技术研究所，北京 100081 |
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| 基金项目： | 中国铁道科学研究院集团有限公司科研项目（2021YJ192） |
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| 摘 要： | 针对城市轨道交通（简称：城轨）列车车厢客流密度检测过程中人群密集、乘客间相互遮挡的问题，文章提出一种基于改进YOLOv5s模型的列车车厢客流密度检测方法。设计了基于车载闭路电视监控（CCTV,Closed-Circuit Television）系统监控进行实时目标检测的列车车厢客流密度检测模型；为解决人群密集及遮挡问题，对YOLOv5s进行优化，采用了双向特征金字塔网络（BiFPN,Bidirectional Feature Pyramid Network）结构加强网络特征融合，设计了一种损失函数计算方法，改进了非极大值抑制（NMS,Non-Maximum Suppression）方法，避免候选框误删除的情况。在标准行人检测数据集和自制地铁车厢乘客数据集上进行实验，结果表明，在两类数据集上，改进模型的检测精度均较原模型有所提升。
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| 关 键 词： | 客流密度 深度学习 YOLOv5s算法 目标检测 BiFPN架构 非极大值抑制(NMS) |
| 收稿时间： | 2022-05-09 |
Detection method of passenger flow density in train carriage based on improved YOLOv5s |
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| Affiliation: | 1.Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China2.Operation Engineering Department, Beijing MTR Corporation Limited, Beijing 100068, China |
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| Abstract: | Aiming at the problem of crowded and serious mutual occlusion among passengers in the process of passenger flow density detection of urban rail transit train carriage, this paper proposed a passenger flow density detection method of train carriage based on improved YOLOv5s model, designed a detection model of passenger flow density in the train compartment based on CCTV (Closed Circuit Television) system monitoring for real-time target detection. In order to solve the problem of crowd density and occlusion, the paper optimized YOLOv5s, used BiFPN (Bi directional Feature Pyramid Network) structure to strengthen network feature fusion, designed a loss function calculation method, and improved NMS (Non Maximum Suppression) method to avoid the false deletion of candidate boxes. The paper conducted experiments on the standard pedestrian detection dataset and the self-made subway carriage passenger data set. The results show that the detection accuracy of the improved model is improved compared with the original model on the two types of datasets. |
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