Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recen...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2017-02, Vol.17 (2), p.336-336
Main Authors: Tang, Tianyu, Zhou, Shilin, Deng, Zhipeng, Zou, Huanxin, Lei, Lin
Format: Article
Language:English
Subjects:
Aerials
Classifiers
Computer vision
convolutional neural networks
Feature maps
hard negative example mining
hyper region proposal network
Image detection
Networks
Neural networks
Proposals
vehicle detection
Vehicles
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Summary:Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.
ISSN:1424-8220
1424-8220
DOI:10.3390/s17020336