NDLI: Fast ship detection combining visual saliency and a cascade CNN in SAR images

Content Provider	IET Digital Library
Author	Xu, Cheng Yin, Chanjuan Wang, Dongzhen Han, Wei
Abstract	In order to realise the fast detection of ships in synthetic aperture radar (SAR) images, a detection method combining visual saliency and a cascade convolutional neural network (CNN) is proposed. First, based on visual saliency, a multiscale spectral residual model is designed for realising the fast detection of ship candidate regions. Then, a cascaded CNN is designed, which consists of two convolution networks, namely, the front-end shallow CNN, which is used to quickly exclude obvious non-ship candidates and classify the ship candidates according to the ship orientation, and the back-end deep CNN, which is used to detect high-probability candidate regions with rotatable boundary boxes. The whole structure can realise the fast detection and precise positioning of ships with an arbitrary orientation. Finally, the authors conduct detailed experiments on the SAR ship image dataset. The experimental results show that the proposed method can effectively improve the detection accuracy of ships, ensuring the detection efficiency in SAR images.
Starting Page	1879
Ending Page	1887
Page Count	9
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 12, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2020.0113
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-08-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Cascade CNN Cascade Convolutional Neural Network Cascaded CNN Computer Vision And Image Processing Technique Convolution Convolution Network Deep CNN Detection Efficiency Detection Method Fast Ship Detection Feature Extraction Front-end Shallow CNN High-probability Candidate Region Image Classification Image Segmentation Multiscale Spectral Residual Model Neural Computing Technique Neural Nets Nonship Candidates Object Detection Optical, Image And Video Signal Processing Radar Equipment Radar Imaging SAR Image SAR Ship Image Dataset Ship Candidate Region Ship Candidates Ship Orientation Ships Synthetic Aperture Radar Synthetic Aperture Radar Image System And Application Visual Saliency
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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