Coimbatore Railway Stretch Sees Fewer Elephant Deaths with AI Surveillance

Tamil Nadu’s AI-powered wildlife monitoring system has helped prevent elephant deaths on a vulnerable railway stretch near Coimbatore, with thousands of real-time alerts enabling train pilots to slow down or stop trains for the safe movement of wild elephants over the past two-and-a-half years.

The artificial intelligence-based camera network, installed along the railway tracks at Puthupathi village in the Madukkarai Forest Range, has generated more than 7,100 alerts on elephant movement since it became operational.

These alerts prompted loco pilots to either reduce speed or halt trains on more than 3,280 occasions, significantly reducing the risk of collisions between trains and elephants. The system forms part of the state government’s efforts to eliminate elephant deaths caused by train accidents in one of Tamil Nadu’s most sensitive wildlife corridors.

Officials say the initiative has so far ensured zero elephant fatalities on the monitored railway stretch while facilitating nearly 9,500 safe elephant crossings. The project combines artificial intelligence, thermal imaging cameras and continuous human monitoring to detect elephant movement near railway tracks in real time.

Once an elephant is detected, alerts are immediately relayed to forest personnel and railway authorities, allowing swift action to prevent accidents. A dedicated control and command centre functions round the clock to coordinate the operation. Forest officials, frontline staff, drone operators and railway personnel work together to monitor elephant movements and respond to alerts.

After receiving information from the AI cameras, forest teams move to the location to prevent elephants from entering the tracks and guide them safely across the railway corridor.

The monitoring system is integrated with railway communication channels. Station masters at nearby railway stations are alerted immediately, following which loco pilots are instructed through wireless communication to slow down trains while elephants cross the tracks.

Forest and railway officials also share live updates on elephant locations through a dedicated messaging platform to ensure coordinated action.

Apart from elephants, the AI-enabled surveillance network has also detected several other wild animals, including gaur, deer and leopards, creating a broader wildlife monitoring system in the region.

The surveillance network has been further strengthened with the deployment of AI-powered drones, which provide aerial monitoring over a wider landscape beyond the fixed thermal cameras.

The drones enhance situational awareness by tracking elephant movement across forests and adjoining railway corridors, enabling authorities to respond more effectively and ensuring safer coexistence between wildlife and railway operations.