Monocular three-dimensional object detection for proximity monitoring in human-machine collision warning systems on construction sites

Monitoring workers’ proximities to avoid struck-by hazards has aroused great concern in construction safety management. Existing methods are either too laborious and costly to apply extensively or lack spatial perception for accurate monitoring. This study proposes a novel framework for proximity monitoring using only an ordinary two-dimensional (2D) camera to realize human-machine collision warning, which integrates a monocular three-dimensional (3D) object detection model and a post-processing classification module to identify four proximity categories: Dangerous, Potentially Dangerous, Concerned, and Safe. A new dataset containing 22,500 virtual and real-world construction images with 3D bounding box annotations has been created and publicly released to facilitate system development and evaluation. Experiments show that the implemented system is rapid-response and camera carrier-independent, achieving promising proximity detection performance on both virtual and real-world data, with mean precision, recall, and F1 scores of approximately 0.8, 0.7, and 0.8, respectively, within a range of 50 meters. This study preliminarily reveals the potential and feasibility of proximity monitoring using only a 2D surveillance camera, providing a new, promising, and affordable way for early warning of human-machine collisions. © 2025 Elsevier Ltd