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Remote Sensing
Volume 14
Issue 12
10.3390/rs14122790
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Article

Few-Shot Multi-Class Ship Detection in Remote Sensing Images Using Attention Feature Map and Multi-Relation Detector

by
Haopeng Zhang
1,2,3,*,
Xingyu Zhang
1,2,3,
Gang Meng
4,
Chen Guo
1,2,3 and
Zhiguo Jiang
1,2,3
1
Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing 102206, China
2
Beijing Key Laboratory of Digital Media, Beijing 102206, China
3
Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beijing 102206, China
4
Beijing Institute of Remote Sensing Information, Beijing 100011, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(12), 2790; https://doi.org/10.3390/rs14122790
Submission received: 18 April 2022 / Revised: 31 May 2022 / Accepted: 7 June 2022 / Published: 10 June 2022
(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing II)
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Abstract

Monitoring and identification of ships in remote sensing images is of great significance for port management, marine traffic, marine security, etc. However, due to small size and complex background, ship detection in remote sensing images is still a challenging task. Currently, deep-learning-based detection models need a lot of data and manual annotation, while training data containing ships in remote sensing images may be in limited quantities. To solve this problem, in this paper, we propose a few-shot multi-class ship detection algorithm with attention feature map and multi-relation detector (AFMR) for remote sensing images. We use the basic framework of You Only Look Once (YOLO), and use the attention feature map module to enhance the features of the target. In addition, the multi-relation head module is also used to optimize the detection head of YOLO. Extensive experiments on publicly available HRSC2016 dataset and self-constructed REMEX-FSSD dataset validate that our method achieves a good detection performance.
Keywords: few-shot learning; multi-class ship detection; deep learning; remote sensing images few-shot learning; multi-class ship detection; deep learning; remote sensing images
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MDPI and ACS Style

Zhang, H.; Zhang, X.; Meng, G.; Guo, C.; Jiang, Z. Few-Shot Multi-Class Ship Detection in Remote Sensing Images Using Attention Feature Map and Multi-Relation Detector. Remote Sens. 2022, 14, 2790. https://doi.org/10.3390/rs14122790

AMA Style

Zhang H, Zhang X, Meng G, Guo C, Jiang Z. Few-Shot Multi-Class Ship Detection in Remote Sensing Images Using Attention Feature Map and Multi-Relation Detector. Remote Sensing. 2022; 14(12):2790. https://doi.org/10.3390/rs14122790

Chicago/Turabian Style

Zhang, Haopeng, Xingyu Zhang, Gang Meng, Chen Guo, and Zhiguo Jiang. 2022. "Few-Shot Multi-Class Ship Detection in Remote Sensing Images Using Attention Feature Map and Multi-Relation Detector" Remote Sensing 14, no. 12: 2790. https://doi.org/10.3390/rs14122790

APA Style

Zhang, H., Zhang, X., Meng, G., Guo, C., & Jiang, Z. (2022). Few-Shot Multi-Class Ship Detection in Remote Sensing Images Using Attention Feature Map and Multi-Relation Detector. Remote Sensing, 14(12), 2790. https://doi.org/10.3390/rs14122790

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