Small parallel residual convolutional neural network and traffic congestion detection

Abstract

In the development process of modern cities, traffic congestion has become an increasingly severe challenge. Image-based traffic congestion detection can help traffic managers grasp the traffic status in real time and help urban residents avoid congested areas, which is of great significance. Based on the advantages and disadvantage of residual networks, this paper introduces residual units as the basic part of the model. In order to increase the model capacity, a parallel mechanism is introduced. At the same time, in order to reduce the time complexity and space complexity of the algorithm, this paper reduces the scale of large convolutional neural network models and proposes a small parallel residual convolutional neural network (SPRCNN) as an image classification model and applied it to traffic congestion detection. This paper conducts experiments on the Traffic net and CCTRIB datasets, and conducts comparative experiments and spatiotemporal complexity analysis. The results show that the method proposed in this paper is superior to existing large pre-trained models.