Safety Equipment

Research on automated safety equipment detection aims to improve workplace safety by using computer vision to monitor the proper use of personal protective equipment (PPE). Current efforts heavily utilize deep learning object detection models, particularly variations of the YOLO architecture, to identify multiple PPE items (e.g., hard hats, vests, gloves) even under challenging conditions like occlusion or distance. These systems show promise for enhancing safety compliance in high-risk industries such as construction and railway work, potentially reducing workplace accidents through real-time monitoring and access control.