Data for "Meteor detection with a new computer vision approach"

A novel computer vision-based meteor head echo detection algorithm is developed to study meteor fluxes and their physical properties, including initial range, range coverage, and radial velocity. The proposed Algorithm for Head Echo Automatic Detection (AHEAD) comprises a feature extraction function and a Convolutional Neural Network (CNN). The former is tailored to identify meteor head echoes, and then a CNN is employed to remove false alarms. In the testing of meteor data collected with the Jicamarca 50 MHz incoherent scatter radar, the new algorithm detects over 180 meteors per minute at dawn, which is two to ten times more sensitive than prior manual or algorithmic approaches, with a false alarm rate less than 1 percent. The present work lays the foundation of developing a fully automatic AI-meteor package that detects, analyzes, and distinguishes among many types of meteor echoes. Furthermore, although initially evaluated for meteor data collected with the Jicamarca VHF incoherent radar, the new algorithm is generic enough that can be applied to other facilities with minor modifications. The CNN removes up to 98 percent of false alarms according to the testing set. We also present and discuss the physical characteristics of meteors detected with AHEAD, including flux rate, initial range, line of sight velocity, Signal-to-Noise Ratio (SNR), and noise characteristics. Our results indicate that stronger meteor echoes are detected at a slightly lower altitude and lower radial velocity than other meteors.

Li, Y., Galindo, F., Urbina, J., Zhou, Q., & Huang, T.-Y. (2022). Meteor detection with a new computer vision approach. Radio Science, 57, e2022RS007515. https://doi.org/10.1029/2022RS007515

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Work Title Data for "Meteor detection with a new computer vision approach"
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  1. Yanlin Li
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Acknowledgments
  1. The study is partially supported by NSF grant AGS-1903346 and AGS-2152109. The raw data used for the analysis can be obtained from the Jicamarca Radio Observatory. T.-Y. Huang acknowledges that her work is supported by (while serving at) the National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Publication Date 2022
DOI doi:10.26207/pf5z-gw87
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Deposited August 25, 2022

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    Acknowledgments
    • The study is partially supported by NSF grant AGS-1903346 and AGS-2152109. The raw data used for the analysis can be obtained from the Jicamarca Radio Observatory. T.-Y. Huang acknowledges that her work is supported by (while serving at) the National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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    Work Title
    • Meteor detection with a new computer vision approach
    • Data for "Meteor detection with a new computer vision approach"
    Related URLs
    • https://doi.org/10.1029/2022RS007515
    Publisher's Statement
    • Li, Y., Galindo, F., Urbina, J., Zhou, Q., & Huang, T.-Y. (2022). Meteor detection with a new computer vision approach. Radio Science, 57, e2022RS007515. https://doi.org/10.1029/2022RS007515

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