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Complex Water Surface Segmentation with RGBP-FCN

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Intelligent Robotics and Applications (ICIRA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10985))

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Abstract

To help unmanned surface vessel analyze the water environment better, this paper proposes RGBP full convolutional network (RGBP-FCN), which is a method based on polarization characteristics combined with a full convolutional network (FCN). It uses image color information (RGB) and polarization degree to segment the complex water surface. Firstly, the polarization degree image is extracted by three images obtained by a polarizer. The polarization degree information and the RGB information together constitute a four-dimensional information of an image which is fed to FCN. Then fine-tuning is performed by using a trained VGG-16 model, and the segmentation result is finally obtained. The accuracy on the test set is 88.19%, which is 3.73% higher than the accuracy of FCN using only RGB image data sets. RGBP-FCN can achieve higher accuracy of the segmentation of complex water surface image.

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Acknowledgments

This work was supported by the Guangdong Innovative and Entrepreneurial Research Team Program under Grant 2014ZT05G304.

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Authors and Affiliations

  1. National Key Laboratory of Science and Technology on Multi-spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China

    Shihao Liu, Quan Chen, Zihan Yue & Jie Ma

  2. Guang dong HUST Industrial Technology Research Institute Guangdong Province Key Lab of Digital Manufacturing Equipment, Wuhan, China

    Shihao Liu, Quan Chen, Zihan Yue & Jie Ma

Authors
  1. Shihao Liu
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  2. Quan Chen
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  3. Zihan Yue
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  4. Jie Ma
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Corresponding author

Correspondence to Jie Ma .

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Editors and Affiliations

  1. University of Newcastle, Callaghan, New South Wales, Australia

    Zhiyong Chen

  2. University of Newcastle, Callaghan, New South Wales, Australia

    Alexandre Mendes

  3. University of Newcastle, Callaghan, New South Wales, Australia

    Yamin Yan

  4. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Shifeng Chen

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Liu, S., Chen, Q., Yue, Z., Ma, J. (2018). Complex Water Surface Segmentation with RGBP-FCN. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds) Intelligent Robotics and Applications. ICIRA 2018. Lecture Notes in Computer Science(), vol 10985. Springer, Cham. https://doi.org/10.1007/978-3-319-97589-4_31

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  • DOI: https://doi.org/10.1007/978-3-319-97589-4_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97588-7

  • Online ISBN: 978-3-319-97589-4

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