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Research on Small Sample Ship Target Detection Based on SAR Image

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Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology (IoTCIT 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1197))

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Abstract

This paper studies the small sample target detection based on SAR images. Aiming at the problem of target semantic information loss in small sample targets, the residual network structure is optimized and improved based on YOLOv3 algorithm, and the data enhancement method is used to increase the number of small sample ships in the dataset. In this paper, three different ship detection data sets are processed, and the public datasets of multi-source SAR satellite ground object types are classified according to the different shooting satellites and polarization methods. A total of ten standard VOC datasets suitable for different algorithms are produced. In this paper, the constructed algorithm is compared with three comparison algorithms. The results show that although the detection performance of YOLOv3 is better than that of RCNN series, the detection accuracy of the two for small sample targets needs to be improved. Aiming at the small sample ship target with complex background in the data set, the detection effect of our algorithm is better. The mAP is used to verify the detection accuracy. The results show that the improved algorithm’s mAP is 2% higher than others.

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Acknowledgement

This work was supported in part by the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China under Grant 61971153, and Natural Science Foundation of Heilongjiang Province (YQ2022E016).

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Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

    Kaiyu Li, Lu Wang, Chunhui Zhao, Haodong Liu & Yuhang Qi

  2. Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin, China

    Lu Wang & Chunhui Zhao

  3. College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, China

    Zhigang Shang

  4. National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin, China

    Zhigang Shang

Authors
  1. Kaiyu Li
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  2. Lu Wang
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  3. Chunhui Zhao
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  4. Zhigang Shang
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  5. Haodong Liu
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  6. Yuhang Qi
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Corresponding author

Correspondence to Lu Wang .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, Hunan, China

    Jian Dong

  2. School of Information Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Long Zhang

  3. School of Information and Control Engineering, China University of Mining and Technolog, Xuzhou, Jiangsu, China

    Deqiang Cheng

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, K., Wang, L., Zhao, C., Shang, Z., Liu, H., Qi, Y. (2024). Research on Small Sample Ship Target Detection Based on SAR Image. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_47

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  • DOI: https://doi.org/10.1007/978-981-97-2757-5_47

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2756-8

  • Online ISBN: 978-981-97-2757-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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