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CAAI International Conference on Artificial Intelligence

CICAI 2021: Artificial Intelligence pp 589–600Cite as

Attention Scale-Aware Deformable Network for Inshore Ship Detection in Surveillance Videos

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13069))

Abstract

Aiming at detecting multi-scale inshore ships in surveillance videos, a one-stage detector namely Attention Scale-aware Deformable Network (ASDN) is proposed in this paper by employing two primary components including Attention Scale-aware Module (ASM) and Deformable Convolutional Network (DCN). Moreover, ASM composed of several branches of convolutions with specially designed kernels and a Convolutional Block Attention Modules (CBAM), is designed for extracting and refining non-local features of multi-scale inshore ships with large aspect ratios at the topmost feature layer. DCN is adopted to capture irregular significant features of ships by modulating input features using parameterized offsets and amplitudes at lateral connections of fine-grained feature pyramid. Experiments conducted on public Seaships7000 dataset demonstrate the contributions of ASM and DCN, and the effectiveness of our method for multi-scale inshore ship detection in surveillance videos in comparison with other Convolutional Neural Network (CNN) based methods, e.g., FPN, Libra R-CNN, SSD, YOLOv3, RefineDet.

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Author information

Authors and Affiliations

  1. National Key Laboratory of Science and Technology on Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China

    Di Liu, Yan Zhang & Yu Zhang

  2. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China

    Yan Zhao

Authors
  1. Di Liu
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  2. Yan Zhang
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  3. Yan Zhao
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  4. Yu Zhang
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Corresponding author

Correspondence to Yan Zhang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Duke University, Durham, NC, USA

    Yiran Chen

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. University of British Columbia, Vancouver, BC, Canada

    Jane Wang

  5. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

  6. Xidian University, Xi’an, China

    Weisheng Dong

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Liu, D., Zhang, Y., Zhao, Y., Zhang, Y. (2021). Attention Scale-Aware Deformable Network for Inshore Ship Detection in Surveillance Videos. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_50

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  • DOI: https://doi.org/10.1007/978-3-030-93046-2_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93045-5

  • Online ISBN: 978-3-030-93046-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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