Safety Helmet Wearing Detection Based on Particle Swarm Optimization YOLOv7
Ziteng Zhang · 2023
In high-risk work environments, such as construction sites, the use of safety helmets is a mandatory safety measure to prevent head injuries. In this research paper, we propose a novel method for detecting the wearing of safety helmets using a combination of Particle Swarm Optimization (PSO) and YOLOv7, a state-of-the-art object detection algorithm. The aim of this method is to address the issue of detecting safety helmet wearing in high-risk work environments. The proposed approach optimizes the hyperparameters of YOLOv7 training using the PSO algorithm, which results in improved accuracy remarkably. To evaluate the proposed method, a dataset of real-world images is used, and it achieved high accuracy, outperforming other state-of-the-art methods. The results demonstrate the potential of the proposed method as a tool for enhancing safety in high-risk work environments by accurately detecting safety helmet wearing.