Jiang Shan, Feng Yuming, Zhang Wei, Liao Xiaofeng, Dai Xiangguang, Onasanya Babatunde Oluwaseun
School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing 404100, China.
Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Wanzhou, Chongqing 404100, China.
Sensors (Basel). 2024 Jul 1;24(13):4272. doi: 10.3390/s24134272.
With the continuous advancement of the economy and technology, the number of cars continues to increase, and the traffic congestion problem on some key roads is becoming increasingly serious. This paper proposes a new vehicle information feature map (VIFM) method and a multi-branch convolutional neural network (MBCNN) model and applies it to the problem of traffic congestion detection based on camera image data. The aim of this study is to build a deep learning model with traffic images as input and congestion detection results as output. It aims to provide a new method for automatic detection of traffic congestion. The deep learning-based method in this article can effectively utilize the existing massive camera network in the transportation system without requiring too much investment in hardware. This study first uses an object detection model to identify vehicles in images. Then, a method for extracting a VIFM is proposed. Finally, a traffic congestion detection model based on MBCNN is constructed. This paper verifies the application effect of this method in the Chinese City Traffic Image Database (CCTRIB). Compared to other convolutional neural networks, other deep learning models, and baseline models, the method proposed in this paper yields superior results. The method in this article obtained an F1 score of 98.61% and an accuracy of 98.62%. Experimental results show that this method effectively solves the problem of traffic congestion detection and provides a powerful tool for traffic management.
随着经济和技术的不断进步，汽车数量持续增加，一些关键道路上的交通拥堵问题日益严重。本文提出了一种新的车辆信息特征图（VIFM）方法和多分支卷积神经网络（MBCNN）模型，并将其应用于基于摄像头图像数据的交通拥堵检测问题。本研究的目的是构建一个以交通图像为输入、拥堵检测结果为输出的深度学习模型。旨在为交通拥堵的自动检测提供一种新方法。本文基于深度学习的方法能够有效利用交通系统中现有的海量摄像头网络，而无需在硬件方面进行过多投入。本研究首先使用目标检测模型识别图像中的车辆。然后，提出了一种提取VIFM的方法。最后，构建了基于MBCNN的交通拥堵检测模型。本文在中国城市交通图像数据库（CCTRIB）中验证了该方法的应用效果。与其他卷积神经网络、其他深度学习模型和基线模型相比，本文提出的方法取得了更优的结果。本文方法获得了98.61%的F1分数和98.62%的准确率。实验结果表明，该方法有效解决了交通拥堵检测问题，为交通管理提供了有力工具。
