The International Association for Automation and Robotics in Construction
Publications / CCC 2025 - Zadar, Croatia
APPLICATION OF MACHINE LEARNING ALGORITHMS IN IDENTIFYING FALLS IN THE WORKPLACE
The construction industry is widely recognised as one of the most hazardous industries due to the complexity of its projects and the rapid implementation of investment. A significant contributing factor to injuries and fatalities in this sector is human falls. The prevalence of accidents at work, particularly those attributed to falls, necessitates the implementation of urgent preventive measures and innovative technological solutions. The objective of the research outlined in this paper is the development of an algorithm capable of detecting human falls in real time. This solution is innovative in its application on construction sites, where its use will facilitate the identification of incidents related to human falls, as well as the provision of rapid assistance to the injured. In the case of such injuries, urgent medical intervention is in most cases crucial to save the health and life of the injured person. The intelligent safety monitoring system is designed to utilise machine learning (ML) algorithms for real-time image analysis with the Python language and the implementation of the proposed algorithm. The YOLOv8 algorithm is specifically responsible for people detection. The proposed detection algorithm is predicated on a two-stage analysis: initial detection of human presence and subsequent verification of whether a human has fallen. In the event of a fall, the developed algorithm automatically generates an alert. The model was tested in construction areas in order to reproduce the natural environment of its target application. A set of images containing both cases of construction worker falls and situations in which no incident occurred was used.