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Journal of Advanced Transportation
Volume 2017 (2017), Article ID 2823617, 10 pages
https://doi.org/10.1155/2017/2823617
Research Article

Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN

1Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
2Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou No. 2, Nanjing 210096, China

Correspondence should be addressed to Guizhen Yu

Received 2 December 2016; Revised 12 July 2017; Accepted 25 July 2017; Published 29 August 2017

Academic Editor: Pascal Vasseur

Copyright © 2017 Yongzheng Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

UAV based traffic monitoring holds distinct advantages over traditional traffic sensors, such as loop detectors, as UAVs have higher mobility, wider field of view, and less impact on the observed traffic. For traffic monitoring from UAV images, the essential but challenging task is vehicle detection. This paper extends the framework of Faster R-CNN for car detection from low-altitude UAV imagery captured over signalized intersections. Experimental results show that Faster R-CNN can achieve promising car detection results compared with other methods. Our tests further demonstrate that Faster R-CNN is robust to illumination changes and cars’ in-plane rotation. Besides, the detection speed of Faster R-CNN is insensitive to the detection load, that is, the number of detected cars in a frame; therefore, the detection speed is almost constant for each frame. In addition, our tests show that Faster R-CNN holds great potential for parking lot car detection. This paper tries to guide the readers to choose the best vehicle detection framework according to their applications. Future research will be focusing on expanding the current framework to detect other transportation modes such as buses, trucks, motorcycles, and bicycles.