目标检测下的车型对交通流速度影响研究

doi:10.3778/j.issn.1002-8331.2307-0389

摘要/Abstract

摘要： 针对传统目标检测跟踪算法识别精度低，实时性差等缺点，提出了一种基于YOLOv5s和DeepSort算法模型的视频车辆实时车流量和速度检测方法。构建了包含25?877个目标样本的数据集，采用YOLOv5s算法模型实现视频车辆的检测，利用DeepSort算法对车辆进行跟踪计数与测速，实现了路段监控对车辆的实时检测。基于深度学习获取的速度与交通量数据，构建了不同车型对交通流速度影响模型，探究畅通状态、稳定状态、拥挤状态下车型的速度与交通量之间的关系，结果表明：算法模型对视频车辆的检测效果良好，平均准确度达到91.4%；不同状态下，车型对交通量速度的影响程度不同；畅通状态和稳定状态下，出租车的速度对交通量影响较大；拥挤状态下，私家车的车速受交通量影响较大。

关键词: 交通管控, 目标检测, 不同车型, 深度学习, 交通流速度模型

Abstract: Aiming at the shortcomings of traditional target detection and tracking algorithms such as low recognition accuracy and poor real-time performance, a real-time traffic flow and speed detection method for video vehicles based on YOLOv5s and DeepSort algorithm models is proposed. A dataset containing 25 877 target samples is constructed, and the YOLOv5s algorithm model is used to realize the detection of video vehicles, and the DeepSort algorithm is used to track and count and measure the speed of vehicles, realizing the real-time detection of vehicles by road section monitoring. Based on the speed and traffic volume data obtained by deep learning, a model of the influence of different models on the speed of traffic flow is constructed to explore the relationship between the speed of the models and the traffic volume in the smooth state, stable state, and congested state, and the results show that：the algorithm model is effective in detecting the video vehicles, and the average accuracy reaches 91.4%. The models have different influence on the speed of the traffic volume in different states. The speed of the taxis in the smooth and stable state, the speed of cab has a greater impact on the traffic volume. Under the congested state, the speed of private cars is more affected by the traffic volume.

Key words: traffic control, target detection, different vehicle types, deep learning, traffic flow speed model

徐慧智, 常梦莹, 陈祎楠, 郝东升. 目标检测下的车型对交通流速度影响研究[J]. 计算机工程与应用, 2024, 60(24): 314-321.

XU Huizhi, CHANG Mengying, CHEN Yinan, HAO Dongsheng. Research on Influence of Vehicle Type on Traffic Flow Speed Under Target Detection[J]. Computer Engineering and Applications, 2024, 60(24): 314-321.

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