Vehicle Detection in Aerial Images

Michael Ying Yang, Wentong Liao, Xinbo Li, Yanpeng Cao, Bodo Rosenhahn

Research output: Contribution to journalArticleAcademicpeer-review

25 Citations (Scopus)
245 Downloads (Pure)


The detection of vehicles in aerial images is widely applied in many applications. Comparing with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem because of small vehicle size and the complex background. In this paper, we propose a novel double focal loss convolutional neural network (DFL-CNN) framework. In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposal network (RPN) and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. We demonstrate the performance of our model on the existing benchmark German Aerospace Center (DLR) 3K dataset as well as the ITCVD dataset. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection.
Original languageEnglish
Pages (from-to)297-304
Number of pages8
JournalPhotogrammetric engineering and remote sensing
Issue number4
Publication statusPublished - 1 Apr 2019



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