Toward Arbitrary-Oriented Ship Detection With Rotated Region Proposal and Discrimination Networks

Ship detection from remote sensing images can provide important information for maritime reconnaissance and surveillance and is also a challenging task. Although previous detection methods including some advanced ones based on deep convolutional neural network expertize in detecting horizontal or ne...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2018-11, Vol.15 (11), p.1745-1749
Main Authors: Zhang, Zenghui, Guo, Weiwei, Zhu, Shengnan, Yu, Wenxian
Format: Article
Language:English
Subjects:
Artificial neural networks
Convolutional neural network (CNN)
Convolutional neural networks
Detection
Discrimination
Faster R-CNN
Feature extraction
Image detection
Marine vehicles
Methods
Neural networks
Orientation
Proposals
Remote sensing
rotated region
Shape
ship detection
Ships
State of the art
Task analysis
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Summary:Ship detection from remote sensing images can provide important information for maritime reconnaissance and surveillance and is also a challenging task. Although previous detection methods including some advanced ones based on deep convolutional neural network expertize in detecting horizontal or nearly horizontal targets, they cannot give satisfying detection results for arbitrary-oriented ship detection. In this letter, we introduce a novel ship detection system that can detect arbitrary-oriented ships. In this method, a rotated region proposal networks (R 2 PN) is proposed to generate multiorientated proposals with ship orientation angle information. In R 2 PN, the orientation angles of bounding boxes are also regressed to make the inclined ship region proposals generated more accurately. For ship discrimination, a rotated region of interest pooling layer is adopted in the following classification subnetwork to extract discriminative features from such inclined candidate regions. The proposed whole ship detection system can be trained end to end. Experimental results conducted on our rotated ship data set and HRSD2016 benchmark demonstrate that our proposed method outperforms state-of-the-art approaches for the arbitrary-oriented ship detection task.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2018.2856921