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Azevedo, C.L., Cardoso, J.L., Ben-Akiva, M., Costeira, J.P. and Marques, M. (2014) Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing. Procedia-Social and Behavioral Sciences (S1877-0428), 111, 849-858. https://doi.org/10.1016/j.sbspro.2014.01.119

has been cited by the following article:

• TITLE: A Vehicle Detection Method for Aerial Image Based on YOLO

  AUTHORS: Junyan Lu, Chi Ma, Li Li, Xiaoyan Xing, Yong Zhang, Zhigang Wang, Jiuwei Xu

  KEYWORDS: Vehicle Detection, Aerial Image, YOLO, VEDAI, COWC, DOTA

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.11, November 19, 2018

  ABSTRACT: With the application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become a key engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial image based on YOLO deep learning algorithm is presented. The method integrates an aerial image dataset suitable for YOLO training by pro-cessing three public aerial image datasets. Experiments show that the training model has a good performance on unknown aerial images, especially for small objects, rotating objects, as well as compact and dense objects, while meeting the real-time requirements.