Indian Railways has deployed a cutting-edge Machine Vision Inspection System (MVIS) on key freight corridors to identify technical faults in real time. The AI-powered system marks a paradigm shift from traditional manual inspection to a high-precision, tech-led approach, aimed at preventing accidents and enhancing operational efficiency.
The MVIS works by using high-speed cameras mounted along the tracks to scan the undercarriage of moving trains. These images are then analysed using artificial intelligence algorithms to detect loose, missing, or misaligned components that could otherwise go unnoticed during manual checks. Once a fault is identified, automated alerts are sent to the engineering teams for prompt intervention. According to senior railway officials, this early-warning system is already being installed across freight routes through a partnership with the Dedicated Freight Corridor Corporation of India Limited (DFCCIL). Plans are underway to extend this intelligent safety network to passenger routes as well, potentially revolutionising rail safety across one of the world’s largest railway systems.
This move is part of Indian Railways’ broader vision to embrace sustainable, tech-integrated infrastructure solutions that reduce human error and increase reliability. While conventional inspections have limitations in terms of reach and efficiency, MVIS offers round-the-clock, real-time surveillance that can proactively flag vulnerabilities long before they escalate into dangerous incidents. The deployment of MVIS also aligns with India’s long-term goals of building smart transport systems that are climate-conscious, digitally enabled, and economically efficient. Experts suggest that such innovations are essential as India’s rail network becomes increasingly dense, with rising freight and passenger loads putting immense pressure on rolling stock and infrastructure.
With the launch of MVIS, Indian Railways signals its intent to modernise and future-proof its operations. More than just a safety measure, it sets the foundation for data-driven maintenance models that can help extend the life of critical assets while safeguarding lives and cargo.
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