Crane operations can be safety hazards to their surroundings on job sites, particularly during blind lifts, when crane and its payload movement is affected due to the reduced visual field of the crane operator. The increased availability of high-definition cameras mounted on unmanned aerial vehicles (UAVs) helps to address this issue as it allows the crane and its surrounding resources, such as workers and their activities, to be monitored in real time. In this paper, we focus on the task of localizing cranes in images obtained from these flying cameras. Our aim is to prove that state-of-the-art object detectors can localize cranes in visual feeds accurately and in real time. To this end, we apply a state-of-the-art object detector to a dataset of crane images captured from a real-world construction site. Preliminary results show that this object detector is effective at pinpointing crane locations in real-time and could be integrated into end-to-end machine vision applications such as payload tracking or 3D crane pose estimation.
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Roberts et al. (2017) studied this question.