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Content Provider | IET Digital Library |
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Author | Shiqi, Chen Ronghui, Zhan Jun, Zhang |
Abstract | Automatic ship detection in SAR imagery has been playing a significant role in the field of marine monitoring but great challenges still exist in real-time application. Despite the exciting progresses made by deep-learning techniques, most detectors failed to yield locations of fairly high quality. Moreover, the ships with variant sizes and aspects are easily omitted especially for small objects under complicated background. To alleviate the above problem, the authors propose an elaborately designed single shot detection framework combined with attention mechanism, which roughly locates the regions of interest via an automatically learned attentional map. This lay the foundation of accurate positioning of extremely small objects since the background interference can be effectively suppressed. Furthermore, a multi-level feature fusion module integrated in top-down and bottom-up manner is adopted to adequately aggregate features from not only adjacent but also distant layers. This strengthens local details and merge strong semantic information, enabling the generation of higher qualified anchors for the efficient detection of multi-scale and multi-orientated objects. Experiments on SAR ship dataset have achieved a promising result, surpassing current state-of-the-art methods. |
Starting Page | 7381 |
Ending Page | 7384 |
Page Count | 4 |
Volume Number | 2019 |
e-ISSN | 20513305 |
Issue Number | Issue 21, Nov (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/21 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0555 |
Journal | The Journal of Engineering |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2019-07-11 |
Access Restriction | Open |
Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
Subject Keyword | Attention Mechanism Automatic Ship Detection Automatically Learned Attentional Map Background Interference Computer Vision And Image Processing Technique Deep-learning Technique Extremely Small Objects Feature Extraction Image Recognition Learning in AI Marine Monitoring Marine Radar Multilevel Feature Fusion Multiorientated Objects Multiscale Objects Neural Computing Technique Neural Nets Object Detection Radar Detection Radar Equipment Radar Imaging Regional Attention-based Single Shot Detector SAR Imagery SAR Ship Dataset SAR Ship Detection Ships Single Shot Detection Strong Semantic Information Synthetic Aperture Radar System And Application |
Content Type | Text |
Resource Type | Article |
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