PPElytics

Abstract

Construction sites are among the most hazardous workplaces, where non-compliance with Personal Protective 
Equipment (PPE) regulations can result in severe injuries and fatalities. Traditional safety monitoring methods 
primarily depend on manual supervision, which is labor-intensive, prone to human error, and ineffective for large
scale construction environments. This paper presents an AI-based PPE Detection and Worker Safety Management 
System that automates safety compliance monitoring using advanced computer vision techniques. The proposed 
system employs the YOLOv8 object detection model to identify workers and detect PPE components such as 
helmets, safety vests, and masks in real time. In addition to PPE detection, the framework incorporates worker 
registration, administrative approval, unique worker identification, violation tracking, and automated policy 
enforcement. An intelligent chatbot interface enables supervisors to access real-time information regarding 
worker attendance, PPE compliance, and safety violations through natural language queries. The system 
maintains comprehensive safety records and identifies repeated violations, thereby supporting proactive decision
making and enhanced workplace safety. By integrating artificial intelligence, worker management, and intelligent 
analytics into a unified platform, the proposed solution improves monitoring efficiency, strengthens regulatory 
compliance, and contributes to a safer construction environment.