Controlling and Managing Safety on The Construction Site by Using Artificial Intelligence Model

Ensuring occupational safety in the construction industry is of paramount importance. Personal protective equipment (PPE) is a critical component of safety measures on construction sites. However, manually monitoring workers’ PPE and identifying violations of safety rules can be time-consuming and challenging. With the advent of artificial intelligence (AI), there is potential for real-time detection of PPE status and site violations, making safety management more efficient. In this technical article, the authors propose a model using YOLOv5 and other algorithms to detect workers’ PPE and identify safety violations in real time. In the case study, the model demonstrates high accuracy and effectiveness compared to other popular object detection algorithms, making it a promising tool for safety management in construction sites. This study contributes to both knowledge and practice in the field of construction safety and highlights the potential of AI for automating safety management tasks. Possible reusable methodology allows future research in this area further improve the accuracy and efficiency of AI models for safety management, reducing the risk of accidents and injuries on construction sites.