The Karelsky Okatysh enterprise has implemented a unique safety control system based on artificial intelligence. The technology analyzes the condition of heavy-duty dump truck drivers in real time, instantly alerting them to signs of fatigue or distraction.
Karelsky Okatysh actively uses modern technologies to improve production safety. One of the key innovations was a monitoring system based on computer vision, which automatically monitors the concentration of attention of drivers of quarry equipment.
A smart algorithm continuously analyzes the operator’s behavior: direction of gaze, head position, seat belt use. At the slightest sign of drowsiness, distraction, or attempting to use the phone artificial intelligence instantly activates the alarm. A warning signal sounds directly in the cabin, and the notification is sent to the consoles of responsible employees – the foreman and the dispatcher.
The software includes several independent modules. Each of them is responsible for a separate task: assessing the level of fatigue based on blink frequency, analyzing concentration, and checking personal protective equipment. All models were developed by internal specialists of Severstal-infocom and Severstal Digital, which provides flexibility of configuration and the possibility of further expansion of functionality.
As company representative Konstantin Korotkin emphasized, the solution allows you to monitor the condition of operators around the clock and effectively prevent potentially dangerous situations. After successful testing in 2022-2023, 50 units of equipment have already been equipped with the technology.
The implementation of an AI system at Karelsky Okatysh demonstrates the real practical benefits of digital innovation in industry. This not only increases discipline, but also creates an additional protective barrier, ensuring the safety of employee health and uninterrupted work processes.
The material was prepared with the support of the Russian Ministry of Education and Science as part of the Decade of Science and Technology.
Source: @dprom
Image generated by a neural network
