Cybercrime is evolving at an alRailways worldwide face a persistent challenge: inspecting high-speed trains thoroughly, accurately, and without disrupting operations. Traditional manual inspections are constrained by visibility, weather, lighting, and the sheer complexity of examining every component in real time.
As a result, small but critical defects—such as missing springs, loose bolts, dragging parts, damaged EM pads, misaligned components, or broken fittings—often go undetected until they evolve into major failures or accidents.
To bridge this gap, Dedicated Freight Corridor Corporation
of India Limited (DFCCIL) has embraced Machine Vision Inspection Systems (MVIS), powered by AI and machine learning. These systems provide continuous, automated, high- precision monitoring capable of detecting anomalies even when trains move at speeds up to 100 km/h. Developed in collaboration with the Indian Institute of Science (IISc), Bengaluru under the ‘Make in India’ and ‘AtmaNirbhar Bharat’ initiatives, MVIS represents a transformative step toward predictive and intelligent railway maintenance.
MVIS installations at New Daudkhan and near Karvandiya on the Eastern Dedicated Freight Corridor (EDFC) use high-speed cameras, line-scan technology, advanced servers, and powerful LED lighting to capture and analyze vast visual datasets
in real time. The system detects a wide range of issues, including EM pad failures, missing springs, axle-end problems, hanging SAB components, wagon-door defects, undercarriage anomalies, and struc- tural irregularities. Each defect is automatically mapped to locomotive and wagon identi-fiers, enabling precise and timely corrective action. Notably, the system has already helped avert incidents involving dragging SAB pull-rod parts, which are notoriously difficult for manual teams to spot.
DFCCIL’s partnership with IISc, formalized in March 2022, includes shared intellectual property and focuses on building long-term indigenous capability. Phase I
of the project involves proof- of-concept work: optimizing cameras and lighting, validating image capture at different speeds, training AI models, and creating dashboards. Phase II will expand detection algorithms, refine hardware, and extend compatibility across various wagon types, ultimately enabling a corridor-wide automated inspection network.
The hardware setup itself is robust and future-ready. Six weather-protected area-scan cameras capture side views,while a line-scan camera between sleepers records detailed underframe imagery. High- intensity LEDs ensure clarity in all weather and lighting conditions. Protective enclosures guard against vibrations, debris, and temperature changes, while surveillance cameras support round-the-clock monitoring.
The addition of a second MVIS near Karvandiya signals a shift from pe-riodic manual checks to smart, condition-based monitoring—ushering in a safer, more efficient, and environmentally conscious future for India’s freight rail corridors.
