  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.
