Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study

Abstract

Over the past decade, global industrial and construction growth has underscored the importance of safety. Yet, accidents continue, often with dire outcomes, despite numerous safety-focused initiatives. Addressing this, this article introduces a novel approach using YOLOv8, a rapid object detection model, for recognizing personal protective equipment (PPE). This method, leveraging computer vision (CV) instead of traditional sensor-based systems, offers an economical, simpler and field-friendly solution. We established the Color Helmet and Vest (CHV) and Safety HELmet dataset with 5K images (SHEL5K) datasets, comprising eight object classes like helmets, vests and goggles, to detect worker-worn PPE. After categorizing the dataset into training, testing and validation subsets, diverse YOLOv8 models were assessed based on metrics including precision, recall and mAP50. Notably, YOLOv8x and YOLOv8l excelled in PPE detection, particularly in recognizing person and vest categories. This innovative CV-driven method promises real-time PPE detection, fortifying worker safety on construction sites.