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Contour Accentuation for Transfer Learning-Based Ship Recognition Method

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Chia-Yu LinAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 61 - 62

https://doi.org/10.1145/3366424.3382697

Published: 20 April 2020 Publication History

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Abstract

This study proposes a ship recognition system which includes intelligent bridge piers and a ship recognition server. The ship recognition server can analyse the contour features of ship images from intelligent bridge piers by the proposed contour accentuation method; the ship image with contour accentuation can be adopted as the inputs of transfer learning-based neural network for ship classification by the proposed transfer learning-based ship recognition method. In practical experiments, the results showed that the proposed transfer learning-based ship recognition method with contour accentuation can obtain higher accuracy, and the accuracy of the proposed method was 97.79%.

References

[1]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Google Scholar

[2]

Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. 2017. Densely Connected Convolutional Networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Google Scholar

[3]

Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. (2015). arXiv:arXiv:1409.1556v6

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Tian YMeng HYuan F(2024)FREGNet: Ship Recognition Based on Feature Representation Enhancement and GCN Combiner in Complex EnvironmentIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.345401625:11(15641-15653)Online publication date: Nov-2024
https://doi.org/10.1109/TITS.2024.3454016
Tian YMeng HYuan FLing YYuan N(2023)Vision Transformer With Enhanced Self-Attention for Few-Shot Ship Target Recognition in Complex EnvironmentsIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.326845572(1-12)Online publication date: 2023
https://doi.org/10.1109/TIM.2023.3268455
Du YSun SQiu SLi SPan MChen C(2021)Intelligent Recognition System Based on Contour Accentuation for Navigation MarksWireless Communications & Mobile Computing10.1155/2021/66310742021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6631074

Index Terms

Contour Accentuation for Transfer Learning-Based Ship Recognition Method
1. Applied computing
  1. Physical sciences and engineering
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Classification and regression trees
      2. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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Association for Computing Machinery

New York, NY, United States

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Tian YMeng HYuan F(2024)FREGNet: Ship Recognition Based on Feature Representation Enhancement and GCN Combiner in Complex EnvironmentIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.345401625:11(15641-15653)Online publication date: Nov-2024
https://doi.org/10.1109/TITS.2024.3454016
Tian YMeng HYuan FLing YYuan N(2023)Vision Transformer With Enhanced Self-Attention for Few-Shot Ship Target Recognition in Complex EnvironmentsIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.326845572(1-12)Online publication date: 2023
https://doi.org/10.1109/TIM.2023.3268455
Du YSun SQiu SLi SPan MChen C(2021)Intelligent Recognition System Based on Contour Accentuation for Navigation MarksWireless Communications & Mobile Computing10.1155/2021/66310742021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6631074

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Data Augmentation Techniques for Transfer Learning-Based Continuous Dysarthric Speech Recognition

Transfer Accent Identification Learning for Enhancing Speech Emotion Recognition

Increasing the Precision of Dysarthric Speech Intelligibility and Severity Level Estimate

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