Construction is one of the three most hazardous industries in Russia, accounting for 16.3% of severe accidents in 2022 according to Rostrud data. Traditional safety monitoring relies on manual inspections, which often fail to prevent violations such as improper use of personal protective equipment or unauthorized access to hazardous zones.
AI-based video analytics systems utilize neural networks to detect workers, safety gear, construction machinery, and environmental hazards like fire or smoke. These systems operate through specific scenarios, such as monitoring restricted crane zones or equipment idle time, and are organized into modules covering safety, perimeter control, and contractor management.
Technical challenges in construction environments include obstructed camera views, poor lighting, adverse weather, and limited network bandwidth. To address these, developers retrain models using site-specific data and perform edge computing on-site to maintain a 97% detection accuracy, transmitting only event data rather than full video streams.
Implementation of video analytics typically requires one week for a pilot project. Best practices include focusing on the most frequent violations, verifying camera coverage, establishing clear response protocols for alerts, and integrating monitoring requirements into contractor agreements to ensure accountability and measurable improvements.
