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Remote Sens. 2017, 9(11), 1170; doi:10.3390/rs9111170

Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

College of Electronic Science, National University of Defense Technology, Changsha 410073, China
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Academic Editors: Anzhi Yue, Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 27 September 2017 / Revised: 3 November 2017 / Accepted: 13 November 2017 / Published: 14 November 2017
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Abstract

Vehicle detection with orientation estimation in aerial images has received widespread interest as it is important for intelligent traffic management. This is a challenging task, not only because of the complex background and relatively small size of the target, but also the various orientations of vehicles in aerial images captured from the top view. The existing methods for oriented vehicle detection need several post-processing steps to generate final detection results with orientation, which are not efficient enough. Moreover, they can only get discrete orientation information for each target. In this paper, we present an end-to-end single convolutional neural network to generate arbitrarily-oriented detection results directly. Our approach, named Oriented_SSD (Single Shot MultiBox Detector, SSD), uses a set of default boxes with various scales on each feature map location to produce detection bounding boxes. Meanwhile, offsets are predicted for each default box to better match the object shape, which contain the angle parameter for oriented bounding boxes’ generation. Evaluation results on the public DLR Vehicle Aerial dataset and Vehicle Detection in Aerial Imagery (VEDAI) dataset demonstrate that our method can detect both the location and orientation of the vehicle with high accuracy and fast speed. For test images in the DLR Vehicle Aerial dataset with a size of 5616 × 3744 , our method achieves 76.1% average precision (AP) and 78.7% correct direction classification at 5.17 s on an NVIDIA GTX-1060. View Full-Text
Keywords: arbitrary-oriented; vehicle detection; single convolutional neural networks (CNN); aerial images; near-real-time arbitrary-oriented; vehicle detection; single convolutional neural networks (CNN); aerial images; near-real-time
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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. (CC BY 4.0).

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Tang, T.; Zhou, S.; Deng, Z.; Lei, L.; Zou, H. Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks. Remote Sens. 2017, 9, 1170.

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