Object unsafe state model performance.

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型YOLOv5n的施工现场危险识别系统。该系统将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模型，实现了轻量化加速与精度提升的双重目标。研究团队基于自建数据集训练了两款超小型模型（单模型体积约2.5 MB），分别用于检测"不安全作业行为"与"不安全物态"，两款模型在平均精度均值@交并比阈值0.5（mAP@0.5）上分别达到93.6%与99.5%。本系统在Jetson Nano B01边缘计算平台完成部署搭建后，其在现场危险检测中的高效性能得到了实测验证。