Human Unsafe Behavior Dataset Distribution.

To reduce the accident rate in the construction industry, an improved YOLOv5n-based hazard recognition system for construction sites is proposed. By incorporating optimization mechanisms such as the ECA attention module, ghost module, SIoU loss, and EIoU–NMS into YOLOv5n, the system achieves both lightweight acceleration and improved accuracy. Two ultrasmall models (approximately 2.5 MBs each) were trained on a self-built dataset to detect “unsafe human behaviors” and “unsafe object conditions,” achieving mAP@0.5 scores of 93.6% and 99.5%, respectively. After deployment on the Jetson Nano B01 edge platform, the system was constructed, and its high efficiency in onsite hazard detection was validated.为降低建筑业事故发生率，本文提出一种基于改进YOLOv5n的施工现场隐患识别系统。通过将ECA注意力模块（ECA attention module）、Ghost模块（ghost module）、SIoU损失函数（SIoU loss）以及EIoU-NMS（EIoU–NMS）等优化机制融入YOLOv5n，该系统实现了轻量化加速与识别精度的双重提升。研究团队基于自建数据集训练了两款超小型模型（单款体积约2.5 MB），分别用于检测「不安全人类行为」与「不安全物体状态」，其mAP@0.5指标分别达到93.6%与99.5%。该系统在Jetson Nano B01边缘计算平台完成部署后，其在施工现场隐患检测中的高效性能得到了验证。