This paper addressed safety risks in the construction industry, emphasizing the prevalent and fatal risk of falls from heights due to floor openings. Although advancements in computer vision and deep learning offer opportunities for automated safety monitoring, challenges such as inaccuracies in object localization and measuring distances to unsafe zones persist. To overcome these issues, a detection method employing convex quadrilateral bounding boxes was presented, taking into account perspective changes from the view of the camera. By leveraging a pretrained pose-estimation model and enhancing the YOLOv7 architecture, the new method precisely identified unsafe areas and generated virtual fences around floor openings. The presented approach resulted in an average precision of 80.55% and an F1-score of 86.49% in alerting dangers, outperforming existing techniques. This paper underscores the effective integration of state-of-the-art computer vision methodologies for practical safety monitoring in construction sites, highlighting its promise in accident prevention.

中文翻译：

---

使用四边形检测和建筑工人姿势估计防止从地板开口处跌落


本文讨论了建筑行业的安全风险，强调了由于地板开口而导致的高处坠落的普遍且致命的风险。尽管计算机视觉和深度学习的进步为自动化安全监控提​​供了机会，但诸如对象定位不准确和测量到不安全区域的距离等挑战仍然存在。为了克服这些问题，提出了一种采用凸四边形边界框的检测方法，并考虑了相机视角的透视变化。通过利用预训练的姿态估计模型并增强 YOLOv7 架构，新方法可以精确识别不安全区域并在地板开口周围生成虚拟围栏。所提出的方法在危险警报方面的平均精度为 80.55%，F1 分数为 86.49%，优于现有技术。本文强调了最先进的计算机视觉方法在建筑工地实际安全监控中的有效集成，强调了其在事故预防方面的前景。