S. Vishnu Sharma
NIT Rourkela researchers have developed an AI-based Multi-Class Vehicle Detection (MCVD) model and a Light Fusion Bi-Directional Feature Pyramid Network (LFBFPN) tool aimed at improving traffic management in developing countries.
The solution was developed taking into account the challenges posed by growing volumes of vehicular movement, say sources from NIT Rourkela.
Computer vision
Led by Prof. Santos Kumar Das, Associate Professor, Department of Electronics & Communication Engineering (ECE), the research team has leveraged an Intelligent Vehicle Detection (IVD) system, which uses computer vision to identify vehicles in images and videos.
This system collects real-time traffic data to optimise traffic flow, reduce congestion, and aid in future road planning, say researchers from NIT Rourkela.
Findings
The findings of this research have been published in the prestigious journal, IEEE Transactions on Intelligent Transportation Systems, in a paper co-authored by Dr. Santos Kumar Das, with his research scholars Mr. Prashant Deshmukh, Mr. Krishna Chaitanya Rayasam, along with Prof. Upendra Kumar Sahoo, from ECE, NIT Rourkela, and Prof. Sudhan Majhi, from IISc Bangalore.
Developed countries
While IVD systems perform well in developed countries with organised traffic, they face challenges in developing nations with mixed traffic.
In countries like India, a wide variety of vehicles—from cars and trucks to cycles, rickshaws, animal carts, and pedestrians—often operate in proximity, making accurate vehicle detection difficult, say researchers from NIT Rourkela.
Traditional methods
Traditional IVD methods, including sensor systems such as radar and Light Detection and Ranging (LiDAR), are effective in controlled environments but struggle in adverse weather conditions such as dust or rain.
Moreover, these systems are expensive to install. Video-based systems hold greater promise, especially for India, but traditional video processing techniques struggle with fast-moving traffic and demand significant computational power.
DL models
Deep learning (DL) models, a type of Artificial Intelligence (AI) that learns from existing data, provide an efficient way to detect vehicles in video feeds.
These models use Convolutional Neural Networks (CNNs) to identify and analyze traffic images, say sources from NIT Rourkela.
However, they often fail to accurately detect vehicles of varying sizes and angles, particularly in busy, mixed-traffic environments.
Additionally, there is a lack of labeled datasets designed for such complex conditions, say sources from NIT Rourkela.
New MCVD model
To address these challenges, Prof. Santos Kumar 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.
Innovation
Prof Santos Kumar Das Associate Professor. Dept. of Electronics & Communication Engineering, NIT Rourkela, said what makes LFBFPN unique is that it uses a simpler method, reducing the complexity of the model without sacrificing its accuracy.
The system then processes the details through another tool called Modified Vehicle Detection Head (MVDH), which helps it accurately detect and classify vehicles in all kinds of traffic situations, he said.
Accuracy
The MCVD model demonstrates an accuracy improvement compared to existing methods, say sources from NIT Rourkela.
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.
Impact
Speaking about the impact of this research, Prof. Das added, “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.
Its use could help improve traffic systems, reduce congestion, and enhance road safety, he said.
Traffic control system
Further work
The research team is further working on developing a traffic control system based on this idea and is also planning to commercialize it through a start-up.
About NIT Rourkela
NIT Rourkela is one of the premier national level institutions for technical education in the country and is funded by the Government of India.
NIT Rourkela has earned 19th position in NIRF Ranking 2024 (Engineering Category), Overall 34th position, 30th position under Research and 9th position in Architecture and Planning category.
The campus of the Institute consists of the Institute buildings, halls of residence and staff colony in over an area of 262 hectares of land.
It is a residential campus offering accommodation to faculty, staff and students. The campus has all the amenities for developing personal, social and academic skills of the student community.
Also Read – NIT Rourkela develops great new technology