AI rail inspection system mistakes snake for track fault during routine scan

An artificial intelligence-powered railway inspection system used by LNER briefly mistook a snake crossing the tracks for a potential rail defect, providing an unusual example of the technology's cautious approach to identifying infrastructure issues.

The incident came to light after LNER Communications Director Stuart Thomas shared an image captured by the Automated Intelligent Video Review (AIVR) system, which had flagged what it believed to be a defect on the railway.

Instead of damaged infrastructure, the system had detected a snake making its way across the track.

Posting on social media, Thomas said:

"This is an incredible picture. The AI track inspection system on an LNER train thought it had found a rail defect. Actually – it was a snake, slithering across the line."

While the incident prompted amusement online, LNER says it also demonstrates how sensitive the technology has become in identifying anything unusual on or around the railway.

AI helping to identify faults before they become failures

The Automated Intelligent Video Review system is installed on a number of LNER trains and continuously monitors both the track and surrounding infrastructure during normal passenger services.

Images and data collected are automatically analysed before being shared with Network Rail, enabling engineers to investigate locations where the system detects potential issues.

The technology forms part of a wider suite of digital monitoring tools, including the Pantograph Damage Assessment System (PANDAS), which assesses the condition of overhead line equipment used to power electric trains.

Together, the systems are designed to identify emerging defects at an early stage, allowing maintenance teams to intervene before faults develop into major failures that disrupt passengers and increase repair costs.

Preventing disruption through early detection

LNER says the technology has already demonstrated significant operational benefits.

Earlier this year, following a major track defect in Cambridgeshire that resulted in more than 10,000 delay minutes, multiple train cancellations and widespread disruption, the AIVR system identified a separate developing fault near Retford before it deteriorated.

Because engineers received an early warning, repairs were completed overnight without affecting train services or causing any passenger delays.

The operator believes proactive inspection using AI can help reduce reactive maintenance, improve reliability and minimise the financial impact of infrastructure failures across the network.

Technology complements, rather than replaces, engineers

Although the snake was incorrectly classified as a possible defect, the incident illustrates an important characteristic of modern AI inspection systems.

Rather than making final engineering decisions, the technology is designed to flag anything that appears unusual, allowing qualified engineers to review the images and determine whether maintenance is actually required.

This precautionary approach inevitably results in the occasional false alert, but rail operators argue that identifying harmless anomalies is preferable to missing a genuine defect that could affect safety or network performance.

As artificial intelligence becomes increasingly embedded across Britain's railway, systems such as AIVR are expected to play a growing role in supporting predictive maintenance, improving infrastructure monitoring and helping Network Rail target inspections more efficiently.

The mistaken identity of one curious reptile may have provided a light-hearted moment, but it also highlights how AI is increasingly acting as an additional pair of eyes across the railway—detecting everything from developing track faults to unexpected wildlife wandering across the line.