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EasyRP-R-CNN: a fast cyclone detection model

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Abstract

Efficient and accurate cyclone detection is essential for avoiding or reducing severe damage in coastal areas. However, traditional detection methods fail to provide accurate results with efficiency. To respond, we propose a convolutional-based cyclone detection framework, EasyRP-R-CNN, focusing on improving efficiency while minimizing the loss of accuracy. Our detection method utilizes satellite cloud images in the visible light band to improve detection accuracy. We designed a new Region of Interest (ROI) selection mechanism, named Easy Region Proposal, and a scale-based ROI grouping module to avoid classifying undersized ROIs. Experimental results demonstrate that our method exhibits satisfactory detection accuracy for cyclones of various intensities, with improved detection efficiency.

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Data availability

The FY-Series satellite dataset is available at https://satellite.nsmc.org.cn/PortalSite/Data/Satellite.aspx, China Meteorological Administration tropical cyclone best track dataset is available at https://tcdata.typhoon.org.cn/zjljsjj.html, and International Best Track Archive for Climate Stewardship dataset is available at https://www.ncei.noaa.gov/products/international-best-track-archive.

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Acknowledgements

This work was partly supported by the National Key R &D Program of China under Grand No. 2021YFE0108400 and partly supported by the National Natural Science Foundation of China under Grant No. 62172294.

Funding

This work was partly supported by the National Key R &D Program of China under Grand No. 2021YFE0108400 and partly supported by the National Natural Science Foundation of China under Grant No. 62172294.

Author information

Authors and Affiliations

  1. Colledge of Intelligence and Computing, Tianjin University, 135 Yaguan Rd, Jinnan, 300350, China

    Xiaoxian Tian, Chongke Bi & Ce Yu

  2. School of Data Science, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China

    Jun Han

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  1. Xiaoxian Tian
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  2. Chongke Bi
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Contributions

X. T. collected data, conducted experimentation, and wrote the main manuscript text. C. B. administrated the project and provided conceptualization and methodology guidance to X. T. All authors reviewed the manuscript.

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Correspondence to Chongke Bi.

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Tian, X., Bi, C., Han, J. et al. EasyRP-R-CNN: a fast cyclone detection model. Vis Comput 40, 4829–4841 (2024). https://doi.org/10.1007/s00371-024-03483-3

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