Feasibility of a Drone-Based On-Site Proximity Detection in an Outdoor Construction Site

Being struck by equipment, such as a heavy machine and vehicle, is one of the leading causes of occupational fatalities in an outdoor construction project. As demonstrated through previous sensor-based research efforts, the proximity detection of workers and active equipment is useful in identifying potential hazards at construction sites. However, attaching sensors to entities can be burdensome for both contractors and workers. Additionally, the measurable ranges of proximity sensors may not be consistent because of the vulnerability to ambient conditions. Against this backdrop, we propose the use of a drone with computer vision techniques for on-site proximity detection. Specifically, the potential for the visualization of a struck-by hazard on air-view image frames is investigated in this paper. The drone can fly over a construction site so that moving entities are persistently captured with a drone-mounted camera while being tracked by a computer vision-based tracking algorithm (i.e., mean shift embedded particle filter). In addition, single view geometry-based rectification is adopted with the known geometry information of a reference object, to reflect a real scene scale and dynamic distortions derived from the drone's altitude and location. Based on the rectified proximity within the image plane, a struck-by hazard zone is defined and visualized in the video, which provides intuitive feedback on whether a worker of interest is within the potentially hazardous area or not. To validate the presented method in terms of accuracy and applicability, a site video from a wastewater treatment plant renovation project was collected. The preliminary test results showed that the defined hazard zone on the drone's image plane can be rectified to reflect reality on the ground plane. The proposed drone-based on-site proximity detection potentially may be used to identify the risk of workers being struck by equipment in an effective way.