A New Multi-Branch Convolutional Neural Network and Feature Map Extraction Method for Traffic Congestion Detection

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 13; p. 4272
• Main Authors: Jiang, Shan, Feng, Yuming, Zhang, Wei, Liao, Xiaofeng, Dai, Xiangguang, Onasanya, Babatunde Oluwaseun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2024
• Subjects: Algorithms; Cameras; Classification; classification model; Deep learning; Doppler effect; feature map; image data; Machine learning; Neural networks; target detection; Traffic congestion; traffic congestion detection; Vehicles; traffic congestion detection; feature map; target detection; image data; classification model
• ISSN: 1424-8220; 1424-8220
• 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.