Data-Driven Worker Detection from Load-View Crane Camera

Cranes as an essential part of the construction machinery, are one of the prominent sources of fatalities in the construction sites. The camera assistant system can contribute significantly to the safety of the crane operation particularly in blind lifts tasks, where the operator highly relies on the load-view camera. In this paper, we address the worker detection from an off-the-shelf load-view crane camera using a data-driven approach. Due to the difficulties in collecting data, we generate five training datasets via a simulation platform to build up the synthetic samples to improve the state-of-the-art detector. Despite the fact that only the simulation data is used as training datasets, the trained network demonstrates the average precision of up to 66.84% in two real-world scenarios.