YOLO V8: An improved real-time detection of safety equipment in different lighting scenarios on construction sites

Abstract In this work we aimed to detect the safety equipment worn by the workers on construction site using the YOLOv8 model. Its a state-of-the-art deep learning model recognized for its speed and accuracy, in detecting objects within dynamic construction environments. Focusing on classes such as Helmet, Vest, Gloves, Human, and Boots, we assess YOLOv8's efficacy in real-time safety hazard detection. The classes have been labelled using the labelImg software for training the model, with that the testing of different images and videos have been carried out. After deploying the trained model, it shows an impressive accuracy rate of approximately 98.017% with the YOLOv8 model, surpassing previous iterations. Additionally, our Recall and Precision values achieve high levels at 94.9% and 94.36% respectively, while the F1 score and mean Average Precision (mAP) values approximate 91% and 91.9% respectively. These robust performance metrics underscore the reliability and effectiveness of YOLOv8 compared to other existing YOLO models, marking a significant advancement in object detection for construction site management.