Evaluating Artificial Intelligence-Based Models for Personal Protective Equipment Usage Detection in Powerline and Renewable Energy Construction Environments

Abstract

Personal Protective Equipment (PPE) compliance is critical in powerline and renewable energy construction projects, yet non-adherence remains common and difficult to monitor manually. This paper presents an automated PPE detection system using an Artificial Intelligence based object detection model applied to actual site overhead drone imagery. The aim was to identify key non-compliance categories such as the non-use of hardhats, reflective vests, long pants, T-shirts and long sleeve shirts. A custom dataset was developed from aerial and ground footage and enhanced through standard annotation and augmentation techniques. Model performance was evaluated using mAP, precision, recall, and confidence-based metrics, demonstrating reliable detection across most PPE classes despite environmental and distance-related challenges. The study shows the potential of AI-assisted, drone-based monitoring to enhance safety oversight on powerline and renewable energy construction sites. It further outlines future work to improve dataset diversity, resolution, and real-time deployment.