Back to Search
Start Over
Regional attentionābased single shot detector for SAR ship detection
- Source :
- The Journal of Engineering (2019)
- Publication Year :
- 2019
- Publisher :
- Institution of Engineering and Technology (IET), 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 :
- Synthetic aperture radar
010504 meteorology & atmospheric sciences
Computer science
Feature extraction
Energy Engineering and Power Technology
deep-learning techniques
02 engineering and technology
sar ship dataset
strong semantic information
01 natural sciences
Radar imaging
0202 electrical engineering, electronic engineering, information engineering
radar detection
Computer vision
Semantic information
automatic ship detection
0105 earth and related environmental sciences
automatically learned attentional map
Artificial neural network
business.industry
feature extraction
sar imagery
Detector
General Engineering
Single shot
object detection
single shot detection
multiorientated objects
Object detection
marine monitoring
radar imaging
neural nets
lcsh:TA1-2040
marine radar
background interference
learning (artificial intelligence)
sar ship detection
020201 artificial intelligence & image processing
Artificial intelligence
multilevel feature fusion
attention mechanism
lcsh:Engineering (General). Civil engineering (General)
business
ships
multiscale objects
regional attention-based single shot detector
Software
synthetic aperture radar
extremely small objects
Subjects
Details
- ISSN :
- 20513305
- Volume :
- 2019
- Database :
- OpenAIRE
- Journal :
- The Journal of Engineering
- Accession number :
- edsair.doi.dedup.....74d0d87d3a8dcd4d1e112b9aeb8c5fe5