A Real-Time Automated Approach for Ensuring Proper Use of Personal Protective Equipment (PPE) in Construction Site

Abstract

Construction sites are one of the most perilous environments where many potential hazards may occur. Even though workers are trained to stay away from potential dangers, there are still many types of risks that can occur within only a few minutes of carelessness. Personal Protective Equipment (PPE) is an important safety measure used to protect construction workers from accidents. However, PPE usage is not strictly enforced among workers due to all kinds of reasons. This paper proposes the combination of deep learning-based object detection and individual detection using geometry relationships analysis to automatically identify non-PPE-use (NPU); i.e., if a worker is wearing hardhat, eye protection visors, dust masks, or both, to help to facilitate the safety monitoring work of construction workers to ensure PPE are appropriately used. The experimental results demonstrate that the approach was capable of detecting NPU workers with high precision (84.13%) and recall rate (93.10%) while ensuring real-time performance (7.95 FPS on average).