YoloGA: An Evolutionary Computation Based YOLO Algorithm to Detect Personal Protective Equipment

Personal Protective Equipment (PPE) detection plays a critical role in ensuring workplace safety and compliance with industrial regulations. Traditional object detection algorithms, such as YOLO (You Only Look Once), provide real-time and accurate detection capabilities but often require extensive manual tuning of hyperparameters and anchor boxes for optimal performance. This paper explores the integration of evolutionary computation with YOLO to develop an adaptive, high-precision PPE detection system. Due to the impossibility of 24-h human supervision, it has long been not easy to guarantee the use of PPE. However, such monitoring may likely be carried out using technological aids or automated programs. The current study outlines a systematic method for tracking employees’ PPEs, like hard hats, safety vests, etc., in real-time using Deep Learning (DL) models constructed on the YOLO architecture. The suggested method employs a small architecture of YOLO (i.e., YOLOv8s) and a Genetic Algorithm (GA) based evolutionary computation for object detection and localization. With this method, we have built a model with a Mean Average Precision (mAP) value of 87.2% on the validation data set and 83.1% on the test data set, highlighting the effectiveness of evolutionary optimization in refining object detection performance. This framework presents a scalable and automated solution for PPE monitoring, contributing to enhanced workplace safety through Artificial Intelligence.