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A Direction-Aware Inshore Ship Detection Method for SAR Images

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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

In areas such as maritime rescue, fisheries management, traffic surveillance, and national defense, ship detection and recognition based on synthetic aperture radar pictures is crucial. Deep learning offers a fresh approach to high-performance detection and recognition for SAR ships in the past few years with the growth of artificial intelligence. This work examines the ship target recognition approach in SAR images and suggests a direction-aware inshore ship detection method in order to meet the challenges of multi-scale ship target detection in SAR images as well as the complicated background of ships stationed in ports. Multiscale features are observed by using the pyramid feature extraction module with attention method. Aiming at the phase ambiguity problem in OBB regression, the direction-aware classification regression head was designed to accurately determines the position and direction of ship targets. Finally, the experimental part verifies that the proposed method reduces the computational complexity of our method and ensuring the detection performance.

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Acknowledgement

This work was supported in part by the Stable Supporting Fund of Science and Technology Laboratory (Grant: KY10800230061), Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China under Grant 61971153, Natural Science Foundation of Heilongjiang Province (YQ2022E016), and Open Fund of The Key Laboratory in China (Grant: MESTA-2021-A001 and JCKY2022207CH09).

Author information

Authors and Affiliations

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

    Haodong Liu, Lu Wang, Chunhui Zhao, Kaiyu Li & Bailiang Sun

  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

  5. Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering University, Ministry of Industry and Information Technology, Harbin, 150001, China

    Zhigang Shang

  6. College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, 150001, China

    Zhigang Shang

  7. Sanya Nanhai Innovation and Development Base of Harbin Engineering University, Sanya, 57202, China

    Zhigang Shang

Authors
  1. Haodong Liu
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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. Kaiyu Li
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  6. Bailiang Sun
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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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Liu, H., Wang, L., Zhao, C., Shang, Z., Li, K., Sun, B. (2024). A Direction-Aware Inshore Ship Detection Method for SAR Images. 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_7

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  • DOI: https://doi.org/10.1007/978-981-97-2757-5_7

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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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