Rapid and accurate automatic fall detection is essential for improving worker safety and reducing the severity of fall-related incidents on construction sites. To address the challenges of real-time detection in complex and obstructed construction environments, this paper develops a specialized dataset for fall scenarios and introduces a skeleton-based AI model called YOSAP-LSTM. This model integrates YOLOv8 for human detection, SORT and AlphaPose for precise tracking of human keypoints, and a 1D CNN-LSTM for classifying falls versus non-falls. This approach achieves an impressive accuracy of 98.66 % (sensitivity: 97.32 %; specificity: 99.10 %), outperforming current fall detection algorithms while maintaining high accuracy under occlusions. Deployed on an edge device (NVIDIA Jetson Xavier NX), the system runs at 6.44 fps, meeting real-time requirements for portable applications. The YOSAP-LSTM model is both robust and practical, offering significant potential for real-world use in construction by enhancing worker safety through timely fall detection in challenging environments.

中文翻译：

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强大的基于骨骼的 AI，可在有遮挡的建筑工地上自动检测多人跌倒


快速准确的自动跌倒检测对于提高工人安全和降低建筑工地跌倒相关事故的严重程度至关重要。为了解决在复杂和受阻的施工环境中进行实时检测的挑战，本文开发了一个用于坠落场景的专用数据集，并引入了一种名为 YOSAP-LSTM 的基于骨骼的 AI 模型。该模型集成了用于人体检测的 YOLOv8、用于精确跟踪人体关键点的 SORT 和 AlphaPose，以及用于分类跌倒与非跌倒的 1D CNN-LSTM。这种方法实现了令人印象深刻的 98.66 % 的准确率（灵敏度：97.32 %;特异性：99.10 %），优于电流跌倒检测算法，同时在遮挡下保持高精度。该系统部署在边缘设备 （NVIDIA Jetson Xavier NX） 上，以 6.44 fps 的速度运行，满足便携式应用程序的实时要求。YOSAP-LSTM 模型既坚固又实用，通过在具有挑战性的环境中及时检测跌倒来提高工人的安全性，从而为建筑中的实际应用提供了巨大的潜力。