New Delhi: An Artificial Intelligence (AI)-based multi-class vehicle detection (MCVD) model can help improve traffic management in developing countries, like India, said researchers at the National Institute of Technology Rourkela (NIT Rourkela) on Friday.
In the paper published in the prestigious journal IEEE Transactions on Intelligent Transportation Systems, the MCVD model demonstrates an accuracy improvement compared to existing methods.
The team tested the model using the Heterogeneous Traffic Labeled Dataset (HTLD), which includes data from several cities across India and is available for public use.
The model’s real-time performance was also evaluated on the Nvidia Jetson TX2, an edge computing device, where it maintained strong speed and accuracy even under challenging weather conditions and with low-resolution images.
“By overcoming the limitations of older models and addressing the unique challenges of mixed traffic, the MCVD model offers a scalable option for real-time vehicle detection in developing countries,” said Prof. Santos Kumar Das, Associate Professor, Dept. of Electronics and Communication Engineering, NIT Rourkela.
“Its use could help improve traffic systems, reduce congestion, and enhance road safety,” Prof. Das added.
The team leveraged an intelligent vehicle detection (IVD) system, which uses computer vision to identify vehicles in images and videos. The system collects real-time traffic data to optimise traffic flow, reduce congestion, and aid in future road planning.
Prof. Das and his team developed the new MCVD model, which uses Video Deinterlacing network (VDnet) to efficiently extract key features from traffic images, even when vehicles vary in size and shape.
They also introduced a specialised tool called Light Fusion Bi-Directional Feature Pyramid Network (LFBFPN) to further refine the extracted details.
The research team is further working on developing a traffic control system based on this idea and is also planning to commercialise it through a start-up.
(IANS)
