Applications of computer vision in railway operations

Object detection in rail systems for maintenance tasks

As rail traffic continues to grow in Europe, the need for reliable infrastructure monitoring has become crucial. Tracks have to be regularly inspected for wear, damage, and misalignment to facilitate safe and smooth operations. This involves checking thousands of kilometers of rail for early signs of defects before they lead to costly disruptions or accidents. Vision AI offers an effective way to automate and enhance this inspection process.

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For instance, in the Netherlands, where the railway network stretches across thousands of kilometers, constantly monitoring and maintaining tracks is a huge task. While manual inspections are still important, they can be slow, labor-intensive, and often disrupt service. To solve this, a computer vision system was introduced to inspect rail-track conditions in real time.

Cameras mounted on moving trains capture a continuous stream of high-resolution images. These visuals are then analyzed by Vision AI models trained to detect track misalignments, worn or damaged components, and missing parts.

With continuous, automated monitoring in place, inspections are not only faster but also more accurate. Maintenance teams can prioritize areas that need attention most, reducing unnecessary checks. As a result, operators have been able to lower operational risks, cut down on delays, and improve overall safety, without interrupting daily service.

Driving safer operations with AI in railway infrastructure

In large-scale rail projects, maintaining on-site safety is one of the most persistent challenges. This was especially true during the construction of Wuhan’s urban rail network in China, where active worksites and shifting conditions required constant oversight.

Traditionally, safety checks relied on manual observation, which often meant risks were only identified after an incident had occurred. To improve response times and reduce risk, researchers introduced a real-time safety monitoring system powered by computer vision.

More than 240 high-definition cameras were installed across the construction sites to record activity continuously. The video footage was processed on-site using AI models capable of detecting not only when individuals entered hazardous areas but also whether nearby equipment was stationary or in motion - an important factor in assessing real-time risk. When unsafe behavior was detected, the system could send immediate alerts to site supervisors.

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A look at railway surveillance using Vision AI

In addition to improving maintenance and safety inspections, computer vision is also being used to monitor rail tracks in real time for intrusions. This is a serious concern, with 995 railroad-related fatalities reported in the United States in 2023 alone. Many of these incidents involved unauthorized access to tracks, an area where traditional monitoring methods are often limited.

To address this, railways in the United States are beginning to adopt computer vision solutions. These systems use live video feeds, analyzed by AI models, to detect events such as trespassing, track obstructions, and potential signal violations. By identifying these issues as they occur, the system helps teams respond quickly, before service is disrupted or safety is put at risk.

Unlike manual inspections, which occur at set intervals, computer vision systems provide continuous, real-time monitoring across the network. When a threat is detected, alerts are sent immediately to operators, allowing for fast, targeted responses. This leads to improved situational awareness, quicker intervention, and fewer delays caused by avoidable incidents.

Food safety monitoring with Vision AI

Every day, millions of meals are prepared and served across trains and stations, where hygiene is a top priority. In India, the Indian Railway Catering and Tourism Corporation (IRCTC) manages one of the largest railway food networks in the world.

Until recently, hygiene checks were done manually, which made it hard to monitor every kitchen all the time. To make things more efficient and reliable, IRCTC introduced a Vision AI system that keeps an eye on food safety practices in real-time.

The system connects to existing CCTV cameras in kitchens and food prep areas. It uses AI models trained to spot common hygiene issues like staff not washing their hands, missing gloves or hairnets, or unclean workstations.

Since it was rolled out, the system has helped improve hygiene standards and made it easier for staff to stay on top of food safety, while spending more time focusing on preparing meals, not just checking procedures.