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A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background
Convolution neural network (CNN)-based detectors have shown great performance on ship detections of synthetic aperture radar (SAR) images. However, the performance of current models has not been satisfactory enough for detecting multiscale ships and small-size ones in front of complex backgrounds. T...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273208/ https://www.ncbi.nlm.nih.gov/pubmed/32365747 http://dx.doi.org/10.3390/s20092547 |
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author | Dai, Wenxin Mao, Yuqing Yuan, Rongao Liu, Yijing Pu, Xuemei Li, Chuan |
author_facet | Dai, Wenxin Mao, Yuqing Yuan, Rongao Liu, Yijing Pu, Xuemei Li, Chuan |
author_sort | Dai, Wenxin |
collection | PubMed |
description | Convolution neural network (CNN)-based detectors have shown great performance on ship detections of synthetic aperture radar (SAR) images. However, the performance of current models has not been satisfactory enough for detecting multiscale ships and small-size ones in front of complex backgrounds. To address the problem, we propose a novel SAR ship detector based on CNN, which consist of three subnetworks: the Fusion Feature Extractor Network (FFEN), Region Proposal Network (RPN), and Refine Detection Network (RDN). Instead of using a single feature map, we fuse feature maps in bottom–up and top–down ways and generate proposals from each fused feature map in FFEN. Furthermore, we further merge features generated by the region-of-interest (RoI) pooling layer in RDN. Based on the feature representation strategy, the CNN framework constructed can significantly enhance the location and semantics information for the multiscale ships, in particular for the small ships. On the other hand, the residual block is introduced to increase the network depth, through which the detection precision could be further improved. The public SAR ship dataset (SSDD) and China Gaofen-3 satellite SAR image are used to validate the proposed method. Our method shows excellent performance for detecting the multiscale and small-size ships with respect to some competitive models and exhibits high potential in practical application. |
format | Online Article Text |
id | pubmed-7273208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72732082020-06-19 A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background Dai, Wenxin Mao, Yuqing Yuan, Rongao Liu, Yijing Pu, Xuemei Li, Chuan Sensors (Basel) Article Convolution neural network (CNN)-based detectors have shown great performance on ship detections of synthetic aperture radar (SAR) images. However, the performance of current models has not been satisfactory enough for detecting multiscale ships and small-size ones in front of complex backgrounds. To address the problem, we propose a novel SAR ship detector based on CNN, which consist of three subnetworks: the Fusion Feature Extractor Network (FFEN), Region Proposal Network (RPN), and Refine Detection Network (RDN). Instead of using a single feature map, we fuse feature maps in bottom–up and top–down ways and generate proposals from each fused feature map in FFEN. Furthermore, we further merge features generated by the region-of-interest (RoI) pooling layer in RDN. Based on the feature representation strategy, the CNN framework constructed can significantly enhance the location and semantics information for the multiscale ships, in particular for the small ships. On the other hand, the residual block is introduced to increase the network depth, through which the detection precision could be further improved. The public SAR ship dataset (SSDD) and China Gaofen-3 satellite SAR image are used to validate the proposed method. Our method shows excellent performance for detecting the multiscale and small-size ships with respect to some competitive models and exhibits high potential in practical application. MDPI 2020-04-30 /pmc/articles/PMC7273208/ /pubmed/32365747 http://dx.doi.org/10.3390/s20092547 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dai, Wenxin Mao, Yuqing Yuan, Rongao Liu, Yijing Pu, Xuemei Li, Chuan A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background |
title | A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background |
title_full | A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background |
title_fullStr | A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background |
title_full_unstemmed | A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background |
title_short | A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background |
title_sort | novel detector based on convolution neural networks for multiscale sar ship detection in complex background |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273208/ https://www.ncbi.nlm.nih.gov/pubmed/32365747 http://dx.doi.org/10.3390/s20092547 |
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