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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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River state classification combining patch-based processing and CNN.

Takahiro Oga1, Ryosuke Harakawa1, Sayaka Minewaki2

  • 1Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, Nagaoka, Japan.

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|December 3, 2020
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Summary

This study introduces a patch-based processing method combined with convolutional neural networks (CNNs) for accurate river state classification from surveillance images. This approach effectively addresses limited data and irrelevant objects, improving flood risk detection.

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Area of Science:

  • Computer Vision
  • Environmental Monitoring
  • Machine Learning

Background:

  • Convolutional Neural Networks (CNNs) require extensive training data, which is often limited for rare events like floods.
  • River surveillance images contain irrelevant objects, hindering direct CNN application for flood risk assessment.

Purpose of the Study:

  • To develop a robust method for classifying river states (flood risk or not) using surveillance camera images.
  • To overcome the limitations of direct CNN application due to data scarcity and irrelevant image content.

Main Methods:

  • A novel patch-based processing technique is proposed to adapt CNNs for river state classification.
  • Patch segmentation increases training data, and relevant patch selection enhances classification accuracy.
  • The patch-based processing and CNN components are developed independently for modularity and ease of maintenance.

Main Results:

  • The proposed method demonstrates effectiveness in classifying river images, distinguishing between muddy and clear states.
  • Experiments using public and real-world surveillance datasets validate the approach for flood warning detection.

Conclusions:

  • The combination of patch-based processing and CNNs provides a feasible and effective solution for river state classification.
  • The modular design allows flexibility in choosing CNN architectures and simplifies system upgrades.