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Article

Multi-Scale Ship Detection Algorithm Based on a Lightweight Neural Network for Spaceborne SAR Images

1
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
2
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Academic Editors: Fernando Bação and Eric Vaz
Remote Sens. 2022, 14(5), 1149; https://doi.org/10.3390/rs14051149
Received: 31 December 2021 / Revised: 14 February 2022 / Accepted: 23 February 2022 / Published: 25 February 2022
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geosciences and Remote Sensing)
The current limited spaceborne hardware resources and the diversity of ship target scales in SAR images have led to the requirement of on-orbit real-time detection of ship targets in spaceborne synthetic aperture radar (SAR) images. In this paper, we propose a lightweight ship detection network based on the YOLOv4-LITE model. In order to facilitate the network migration to the satellite, the method uses MobileNetv2 as the backbone feature extraction network of the model. To solve the problem of ship target scale diversity in SAR images, an improved receptive field block (RFB) structure is introduced, enhancing the feature extraction ability of the network, and improving the accuracy of multi-scale ship target detection. A sliding window block method is designed to detect the whole SAR image, which can solve the problem of image input. Experiments on the SAR ship dataset SSDD show that the detection speed of the improved lightweight network could reach up to 47.16 FPS, with the mean average precision (mAP) of 95.03%, and the model size is only 49.34 M, which demonstrates that the proposed network can accurately and quickly detect ship targets. The proposed network model can provide a reference for constructing a spaceborne real-time lightweight ship detection network, which can balance the detection accuracy and speed of the network. View Full-Text
Keywords: real-time ship detection; SAR images; YOLO; lightweight network real-time ship detection; SAR images; YOLO; lightweight network
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MDPI and ACS Style

Liu, S.; Kong, W.; Chen, X.; Xu, M.; Yasir, M.; Zhao, L.; Li, J. Multi-Scale Ship Detection Algorithm Based on a Lightweight Neural Network for Spaceborne SAR Images. Remote Sens. 2022, 14, 1149. https://doi.org/10.3390/rs14051149

AMA Style

Liu S, Kong W, Chen X, Xu M, Yasir M, Zhao L, Li J. Multi-Scale Ship Detection Algorithm Based on a Lightweight Neural Network for Spaceborne SAR Images. Remote Sensing. 2022; 14(5):1149. https://doi.org/10.3390/rs14051149

Chicago/Turabian Style

Liu, Shanwei, Weimin Kong, Xingfeng Chen, Mingming Xu, Muhammad Yasir, Limin Zhao, and Jiaguo Li. 2022. "Multi-Scale Ship Detection Algorithm Based on a Lightweight Neural Network for Spaceborne SAR Images" Remote Sensing 14, no. 5: 1149. https://doi.org/10.3390/rs14051149

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