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Regional attention-based single shot detector for SAR ship detection
- Source :
- The Journal of Engineering (2019)
- Publication Year :
- 2019
- Publisher :
- Wiley, 2019.
-
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.
- Subjects :
- object detection
feature extraction
synthetic aperture radar
learning (artificial intelligence)
radar imaging
ships
radar detection
marine radar
neural nets
multiorientated objects
sar ship dataset
regional attention-based single shot detector
sar ship detection
automatic ship detection
sar imagery
marine monitoring
attention mechanism
automatically learned attentional map
background interference
deep-learning techniques
single shot detection
extremely small objects
multilevel feature fusion
strong semantic information
multiscale objects
Engineering (General). Civil engineering (General)
TA1-2040
Subjects
Details
- Language :
- English
- ISSN :
- 20513305
- Database :
- Directory of Open Access Journals
- Journal :
- The Journal of Engineering
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.815a4121bac348949c80f8a13f08bd45
- Document Type :
- article
- Full Text :
- https://doi.org/10.1049/joe.2019.0555