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arXiv 2610.05756cs.CV

建筑工地安全帽合规检测的视觉赋能系统

Vision-enabled detection of safety helmet compliance in construction zones

Tri Nhut Do*, Ba Loc Pham

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中文总结 AI 辅助

本文提出基于YOLOv8的视觉系统,实时检测建筑工地安全帽佩戴情况,精度超97%,显著降低头部受伤风险,推动智能施工安全管理。

中文摘要 AI 辅助

在快速发展的施工管理领域中,工人安全始终是首要任务。本文介绍了一种创新的基于视觉的系统,用于实时检测安全帽合规情况,该系统专为建筑工地设计,利用YOLO(You Only Look Once,你只看一次)框架中的先进计算机视觉技术和机器学习算法。我们的系统利用战略位置摄像头的超高分辨率视频流,监控关于安全帽使用的安全法规遵守情况。通过采用深度学习方法,系统能有效识别未佩戴安全帽的人员,从而显著降低工人头部受伤的风险。我们的训练和验证结果显示,在mAP@0.5下,对佩戴和未佩戴安全帽人员的精度均超过97%。此外,我们的实验证明了卓越的检测准确性,展示了系统在不同光照条件和多样化工人动作下的鲁棒性。训练过程中损失的持续下降和指标的持续提升验证了YOLOv8模型在增强识别性能方面的有效性。这项研究的意义超越了单纯的法规合规,为职业安全管理中的创新应用开辟了途径。本研究强调了技术在保护生命中的关键作用,并为智能建筑环境的未来发展奠定了基础。

英文摘要

In the rapidly evolving field of construction management, worker safety remains a top priority. This paper introduces an innovative vision-based system for real-time detection of helmet compliance, specifically designed for construction sites, utilizing advanced computer vision techniques and machine learning algorithms within the YOLO (you only look once) framework. Our system leverages high-resolution video feeds from strategically positioned cameras to monitor adherence to safety regulations regarding helmet usage. By employing deep learning methodologies, the system effectively identifies individuals not wearing helmets, thereby significantly mitigating the risk of head injuries among workers. Our training and validation results revealed an impressive precision exceeding 97% at mAP@0.5 for both helmeted and non-helmeted individuals. Furthermore, our experiments demonstrate exceptional detection accuracy, demonstrating the system's resilience under varying lighting conditions and diverse worker movements. The consistent decrease in loss and improvement in metrics throughout training validates the effectiveness of the YOLOv8 model in enhancing recognition performance. The implications of this research extend beyond mere regulatory compliance, opening avenues for innovative applications in occupational safety management. This study highlights the critical role of technology in protecting lives and lays the groundwork for future advancements in smart construction environments.

发表机构

  • University of Information Technology(信息技术大学)
  • Vietnam National University - Ho Chi Minh City(越南胡志明市国家大学)
  • Thu Dau Mot University(土龙木大学)

机构由 AI 辅助整理,请以论文原文为准。

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