Real-Time Traffic Classification through Deep Learning
Maxim Priymak, Richard Sinnott · 2021
The increasing urbanization of the global population has drawn many researchers’ attention to the field of Intelligent Transportation Systems. Numerous hardware and software technologies have been developed to aid in monitoring and managing the flow of traffic on road networks. As digital cameras become increasingly cheaper and able to produce higher quality images, automated video-based traffic management systems can provide a low cost alternative to conventional (expensive) traffic monitoring systems. In this work we evaluate diverse state-of-the-art deep-learning-based vehicle recognition frameworks on datasets containing surveillance footage of heterogeneous and representative traffic data from Melbourne’s road network. We find that the YOLOv5 family of models offers the optimal balance between detection accuracy, model size, and real-time detection capability for resource-constrained traffic monitoring devices.