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Towards AI-Based Traffic Counting System with Edge Computing
Journal of Advanced Transportation ( IF 2.3 ) Pub Date : 2021-06-28 , DOI: 10.1155/2021/5551976
Duc-Liem Dinh 1 , Hong-Nam Nguyen 1 , Huy-Tan Thai 1 , Kim-Hung Le 1
Affiliation  

The recent years have witnessed a considerable rise in the number of vehicles, which has placed transportation infrastructure and traffic control under tremendous pressure. Yielding timely and accurate traffic flow information is essential in the development of traffic control strategies. Despite the continual advances and the wealth of literature available in intelligent transportation system (ITS), there is a lack of practical traffic counting system, which is readily deployable on edge devices. In this study, we introduce a low-cost and effective edge-based system integrating object detection models to perform vehicle detecting, tracking, and counting. First, a vehicle detection dataset (VDD) representing traffic conditions in Vietnam was created. Several deep learning models for VDD were then examined on two different edge device types. Using this detection, we presented a lightweight counting method seamlessly combining with a traditional tracking method to increase counting accuracy. Finally, the traffic flow information is obtained based on counted vehicle categories and their directions. The experiment results clearly indicate that the proposed system achieves the top inference speed at around 26.8 frames per second (FPS) with 92.1% accuracy on the VDD. This proves that our proposal is capable of producing high-accuracy traffic flow information and can be applicable to ITS in order to reduce labor-intensive tasks in traffic management.

中文翻译:

迈向具有边缘计算的基于人工智能的交通计数系统

近年来,汽车保有量大幅增加,给交通基础设施和交通管制带来了巨大压力。提供及时准确的交通流量信息对于制定交通控制策略至关重要。尽管智能交通系统 (ITS) 不断取得进步且文献丰富,但仍缺乏实用的交通计数系统,可轻松部署在边缘设备上。在这项研究中,我们引入了一种低成本且有效的基于边缘的系统,该系统集成了对象检测模型来执行车辆检测、跟踪和计数。首先,创建了代表越南交通状况的车辆检测数据集 (VDD)。然后在两种不同的边缘设备类型上检查了 VDD 的几个深度学习模型。使用这种检测,我们提出了一种轻量级的计数方法,与传统的跟踪方法无缝结合,以提高计数的准确性。最后,根据统计的车辆类别及其方向获得交通流信息。实验结果清楚地表明,所提出的系统以每秒 26.8 帧 (FPS) 的速度实现了最高推理速度,在 VDD 上的准确率为 92.1%。这证明我们的建议能够产生高精度的交通流量信息,并且可以应用于 ITS,以减少交通管理中的劳动密集型任务。交通流信息是根据统计的车辆类别和方向获得的。实验结果清楚地表明,所提出的系统以每秒 26.8 帧 (FPS) 的速度实现了最高推理速度,在 VDD 上的准确率为 92.1%。这证明我们的建议能够产生高精度的交通流量信息,并且可以应用于 ITS,以减少交通管理中的劳动密集型任务。交通流信息是根据统计的车辆类别和方向获得的。实验结果清楚地表明,所提出的系统以每秒 26.8 帧 (FPS) 的速度实现了最高推理速度,在 VDD 上的准确率为 92.1%。这证明我们的建议能够产生高精度的交通流量信息,并且可以应用于 ITS,以减少交通管理中的劳动密集型任务。
更新日期:2021-06-28
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