Detection Method for Safety Helmet Wearing on Construction Sites Based on UAV Images and YOLOv8

Abstract

With the increasing demand for safety management on construction sites, traditional manual inspection methods for detecting helmet usage face challenges such as low efficiency, limited coverage, and strong subjectivity, making them inadequate for modern construction site safety requirements. To address these issues, this study proposes a helmet detection method based on unmanned aerial vehicles (UAVs) and the YOLOv8 object detection algorithm. The method utilizes UAVs to flexibly capture construction site images, combined with the optimized YOLOv8s model, and employs transfer learning to annotate and train labels for “person” and “helmet”. Additionally, to improve detection accuracy, the study introduces matching criteria and a time-window strategy to further reduce false positives and negatives. Experimental results demonstrate that the proposed method can achieve efficient and accurate helmet usage detection in diverse construction site scenarios, significantly enhancing the automation and reliability of site safety management. Despite its excellent performance, future research should focus on optimizing real-time adaptability and improving performance in complex environments. This study provides an innovative and efficient technical solution for construction site safety management, contributing to the creation of safer and more efficient construction environments.