An AI-based rail inspection system installed on LNER trains in the United Kingdom flagged a possible rail defect. In reality, the image it analyzed showed a snake crossing the tracks.
A artificial intelligence-based railway infrastructure monitoring system provided an unusual moment on the British network: it mistook a snake for a rail defect.
The incident was reported by Stuart Thomas, communications director at London North Eastern Railway (LNER), a British long-distance train operator on the East Coast Main Line.
“It’s an incredible image. The AI-based track inspection system on an LNER train thought it had found a rail defect. In fact, it was a snake slithering across the tracks,” he wrote on X.
The post was accompanied by an image captured by the monitoring system, showing the reptile crossing the tracks.
System Used on Several LNER Trains
An LNER spokesperson explained that the technology is installed on several of the operator’s trains, and the information is transmitted to Network Rail, the UK’s rail infrastructure manager.
The goal is for technical teams to be able to identify areas requiring attention early on and take action before problems lead to delays or major disruptions.
LNER uses systems such as the Pantograph Damage Assessment System (Pandas), designed to assess damage to pantographs and the overhead contact line, and Automated Intelligent Video Review (AIVR), an automated video analysis system based on artificial intelligence.
These technologies constantly monitor the overhead contact line, power supply equipment, and track conditions, flagging potential defects to the operator and the infrastructure manager.
When AI Helps Prevent Delays
Although the snake incident attracted attention due to its unusual nature, LNER claims that the system is already yielding concrete results in preventing railway incidents.
The operator cited the example of a track defect reported in January 2026 by a train engineer in Cambridgeshire. The incident caused over 10,000 minutes of delays, multiple cancellations, and a full day of disruptions for passengers.
A week after that incident, the AIVR system identified a minor fault near Retford, which could have developed into a more serious problem. The report allowed engineers to make the repair overnight, without any delays for passengers.
Such systems are used for predictive maintenance, an increasingly important trend in the rail sector.
Instead of problems being discovered only after a malfunction or delay occurs, the images and data collected by trains in service can indicate in advance the areas where intervention is needed.
A False Positive Involving Reptiles
For LNER, the incident was more of a curiosity than an operational problem. No service disruptions were reported, and the image quickly became an amusing example of the situations that automated inspection systems can encounter.
Beyond this unusual moment, the technology remains useful for the early detection of problems on the network. It’s just that, sometimes, a snake—literally—can slip in between a track anomaly and a potential defect.
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