부산대학교 도서관

Large-scale disaster occurs all over the world frequently, disconnects telecommunications, and destroys communication equipment. In recent years, unmanned aerial vehicles (UAVs) network systems have been studied to work on the reconstruction activities safely and flexibly. The more means of telecommunication, the better because the UAV networks are used for emergency communication. Therefore, this paper studies optical camera communication (OCC) systems using RGB-LED-mounted drones and a high-speed camera for disaster recovery and proposes the RGB-LED-mounted drone’s detection scheme and the signal equalization technique to suppress the RGB interference. We detect the drone using the algorithm of a deep neural network (DNN) based object detection called YOLOv3. This paper adds a new function to reduce the frame rate in object detection. Consequently, the proposed scheme reduces the frame rate to a rate that can conduct real-time operations less than 20 fps from 600 fps. Moreover, the experimental results indicate the feasibility of the proposed scheme that can communicate in error-free operation at a 300-m distance.