Version 1
: Received: 25 August 2021 / Approved: 26 August 2021 / Online: 26 August 2021 (12:00:22 CEST)
How to cite:
Liu, H.; Xu, H.; Zhang, L.; Lu, W.; Yang, F.; Pan, J. SAR Ship Detection Based on Improved Libra RetinaNet. Preprints2021, 2021080509. https://doi.org/10.20944/preprints202108.0509.v1
Liu, H.; Xu, H.; Zhang, L.; Lu, W.; Yang, F.; Pan, J. SAR Ship Detection Based on Improved Libra RetinaNet. Preprints 2021, 2021080509. https://doi.org/10.20944/preprints202108.0509.v1
Liu, H.; Xu, H.; Zhang, L.; Lu, W.; Yang, F.; Pan, J. SAR Ship Detection Based on Improved Libra RetinaNet. Preprints2021, 2021080509. https://doi.org/10.20944/preprints202108.0509.v1
APA Style
Liu, H., Xu, H., Zhang, L., Lu, W., Yang, F., & Pan, J. (2021). SAR Ship Detection Based on Improved Libra RetinaNet. Preprints. https://doi.org/10.20944/preprints202108.0509.v1
Chicago/Turabian Style
Liu, H., Fei Yang and Jingchang Pan. 2021 "SAR Ship Detection Based on Improved Libra RetinaNet" Preprints. https://doi.org/10.20944/preprints202108.0509.v1
Abstract
Maritime ship monitoring plays an important role in maritime transportation. Fast and accurate detection of maritime ship is the key to maritime ship monitoring. The main sources of marine ship images are optical images and synthetic aperture radar (SAR) images. Different from natural images, SAR images are independent to daylight and weather conditions. Traditional ship detection methods of SAR images mainly depend on the statistical distribution of sea clutter, which leads to poor robustness. As a deep learning detector, RetinaNet can break this obstacle, and the problem of imbalance on feature level and objective level can be further solved by combining with Libra R-CNN algorithm. In this paper, we modify the feature fusion part of Libra RetinaNet by adding a bottom-up path augmentation structure to better preserve the low-level feature information, and we expand the dataset through style transfer. We evaluate our method on the publicly available SAR dataset of ship detection with complex backgrounds. The experimental results show that the improved Libra RetinaNet can effectively detect multi-scale ships through expansion of the dataset, with an average accuracy of 97.38%.
Keywords
synthetic aperture radar; deep learning; data augmentation; object detection; ship detection
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
