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http://dx.doi.org/10.7848/ksgpc.2020.38.4.353

Application of Deep Learning Method for Real-Time Traffic Analysis using UAV  

Park, Honglyun (School of Drone & Transportation Engineering, Youngsan University)
Byun, Sunghoon (School of Computer Engineering, Youngsan University)
Lee, Hansung (School of Computer Engineering, Youngsan University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.38, no.4, 2020 , pp. 353-361 More about this Journal
Abstract
Due to the rapid urbanization, various traffic problems such as traffic jams during commute and regular traffic jams are occurring. In order to solve these traffic problems, it is necessary to quickly and accurately estimate and analyze traffic volume. ITS (Intelligent Transportation System) is a system that performs optimal traffic management by utilizing the latest ICT (Information and Communications Technology) technologies, and research has been conducted to analyze fast and accurate traffic volume through various techniques. In this study, we proposed a deep learning-based vehicle detection method using UAV (Unmanned Aerial Vehicle) video for real-time traffic analysis with high accuracy. The UAV was used to photograph orthogonal videos necessary for training and verification at intersections where various vehicles pass and trained vehicles by classifying them into sedan, truck, and bus. The experiment on UAV dataset was carried out using YOLOv3 (You Only Look Once V3), a deep learning-based object detection technique, and the experiments achieved the overall object detection rate of 90.21%, precision of 95.10% and the recall of 85.79%.
Keywords
UAV(Unmanned Aerial Vehicle); ITS(Intelligent Transportation System); Traffic Analysis; Deep Learning; Object Detection;
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Times Cited By KSCI : 3  (Citation Analysis)
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