Ship Detection Based on Faster R-CNN Using Range-Compressed Airborne Radar Data Article Swipe
Tamara Loran
,
André Barros Cardoso da Silva
,
Sushil Kumar Joshi
,
Stefan V. Baumgartner
,
Gerhard Krieger
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/lgrs.2022.3229141
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/lgrs.2022.3229141
Near real-time ship monitoring is crucial for ensuring safety and security at sea. Established ship monitoring systems are the automatic identification system (AIS) and marine radars. However, not all ships are committed to carry an AIS transponder and the marine radars suffer from limited visibility. For these reasons, airborne radars can be used as an additional \nand supportive sensor for ship monitoring, especially on the open sea. State-of-the-art algorithms for ship detection in radar imagery are based on constant false alarm rate (CFAR). Such algorithms are pixel-based and therefore it can be challenging in practice to achieve near real-time detection. This letter presents two object-oriented ship detectors based on the faster region-based convolutional neural network (R-CNN). The first detector operates in time domain and the second detector operates in Doppler domain of airborne range-compressed (RC) radar data patches. The Faster R-CNN models are trained on thousands of real X-band airborne RC radar data patches containing several ship signals. The robustness of the proposed object-oriented ship detectors are tested on multiple scenarios, showing high recall performance of the models even in very dense multi-target scenarios in the complex inshore environment of the North Sea.
Related Topics
Concepts
Computer science
Remote sensing
Radar
Object detection
Detector
Constant false alarm rate
Radar imaging
Convolutional neural network
Robustness (evolution)
Transponder (aeronautics)
Sea state
Real-time computing
Artificial intelligence
Telecommunications
Pattern recognition (psychology)
Engineering
Geology
Gene
Aerospace engineering
Chemistry
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/lgrs.2022.3229141
- https://ieeexplore.ieee.org/ielx7/8859/10034981/09984160.pdf
- OA Status
- hybrid
- Cited By
- 30
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313150818
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4313150818Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/lgrs.2022.3229141Digital Object Identifier
- Title
-
Ship Detection Based on Faster R-CNN Using Range-Compressed Airborne Radar DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-14Full publication date if available
- Authors
-
Tamara Loran, André Barros Cardoso da Silva, Sushil Kumar Joshi, Stefan V. Baumgartner, Gerhard KriegerList of authors in order
- Landing page
-
https://doi.org/10.1109/lgrs.2022.3229141Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/8859/10034981/09984160.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/8859/10034981/09984160.pdfDirect OA link when available
- Concepts
-
Computer science, Remote sensing, Radar, Object detection, Detector, Constant false alarm rate, Radar imaging, Convolutional neural network, Robustness (evolution), Transponder (aeronautics), Sea state, Real-time computing, Artificial intelligence, Telecommunications, Pattern recognition (psychology), Engineering, Geology, Gene, Aerospace engineering, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
30Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 13, 2023: 5Per-year citation counts (last 5 years)
- References (count)
-
17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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