Accident of struck-by machines is one of the leading causes of casualties on construction sites. Monitoring workers' proximities to avoid human-machine collisions has aroused great concern in construction safety management. Existing methods are either too laborious and costly to apply extensively, or lacking spatial perception for accurate monitoring. Therefore, this study proposes a novel framework for proximity monitoring using only an ordinary 2D camera to realize real-time human-machine collision warning, which is designed to integrate a monocular 3D object detection model to perceive spatial information from 2D images and a post-processing classification module to identify the proximity as four predefined categories: Dangerous, Potentially Dangerous, Concerned, and Safe. A virtual dataset containing 22000 images with 3D annotations is constructed and publicly released to facilitate the system development and evaluation. Experimental results show that the trained 3D object detection model achieves 75% loose AP within 20 meters. Besides, the implemented system is real-time and camera carrier-independent, achieving an F1 of roughly 0.8 within 50 meters under specified settings for machines of different sizes. This study preliminarily reveals the potential and feasibility of proximity monitoring using only a 2D camera, providing a new promising and economical way for early warning of human-machine collisions.

翻译：机器撞击事故是建筑工地人员伤亡的主要原因之一。监测工人与机器的距离以避免人机碰撞已引起建筑施工安全管理领域的高度重视。现有方法要么因过于繁琐且成本高昂而难以广泛应用，要么缺乏空间感知能力导致监测精度不足。为此，本研究提出一种仅使用普通2D摄像头实现实时人机碰撞预警的近距监测新框架。该框架融合单目3D目标检测模型（用于从2D图像中提取空间信息）与后处理分类模块（将近距状态划分为危险、潜在危险、关注、安全四类）。为促进系统开发与评估，研究构建并公开了包含22000张带3D标注图像的虚拟数据集。实验结果表明，训练后的3D目标检测模型在20米范围内宽松平均精度（loose AP）达75%。此外，该实现系统具有实时性与摄像头载体无关性，在指定参数条件下对不同尺寸设备于50米距离范围内的检测F1值约为0.8。本研究初步揭示了仅使用2D摄像头实现近距监测的潜力与可行性，为建筑工地人机碰撞预警提供了兼具创新性与经济性的新途径。