Researchers at NIT Rourkela have developed an AI-based vehicle detection model, MCVD (Multi-Class Vehicle Detection), to tackle traffic congestion and improve road safety, particularly in developing countries like India.
The MCVD model leverages advanced computer vision and deep learning techniques to accurately detect and classify different types of vehicles in real time. Unlike conventional traffic monitoring systems, the AI-driven model provides higher accuracy, faster processing speeds, and better adaptability to complex traffic conditions, making it a game-changer for urban mobility management.
Dr. Rajeev Kumar, lead researcher of the project, stated, “This model is designed to work efficiently even in high-density traffic scenarios, where traditional methods struggle. With its integration into existing traffic control systems, authorities can optimize signal timings, enforce traffic rules, and reduce congestion-related issues.”
The AI-powered system is expected to benefit traffic management agencies by automating vehicle detection, easing manual workload, and providing crucial data for urban planning. Researchers at NIT Rourkela are now collaborating with local authorities to conduct pilot tests before a large-scale rollout.
With growing concerns over road safety and traffic inefficiencies in India and other developing nations, AI-powered solutions like MCVD could play a crucial role in ensuring smoother, safer, and smarter traffic management.
NIT Rourkela Develops AI-Powered Vehicle Detection Model for Smarter Traffic Management
