A UAS-Based Work Zone Safety Monitoring System by Integrating Internal Traffic Control Plan (ITCP) and Automated Object Detection in Game Engine Environment

Current practices are limited to effectively identify unsafe conditions due to a lack of integration between internal traffic control plans (ITCP) that can guide safe activities in construction worksites and safety monitoring systems. To address the limitation, this study proposed the novel concept of a safety monitoring system by leveraging unmanned aircraft systems (UAS), game engine-based ITCP, and deep learning. In this study, workers and equipment were automatically recognized through object detection from aerial images. Through the case study, this proposed concept was validated to monitor the unsafe activities of workers by four rules established. While the limitations of this study were documented, such as the low number of aerial images to train deep learning model, the low performance of object detection, and the errors that occurred when augmenting the detection results in the game engine, this study also emphasized the potential of the proposed digital ITCP-based safety monitoring system.