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 2 to 10 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, and noise characteristics. Our results indicate that stronger meteor echoes are detected at a slightly lower altitude and lower radial velocity than other meteors.

An edited version of this paper was published by AGU. Copyright (year) American Geophysical Union [Meteor Detection With a New Computer Vision Approach. Radio Science 57, 10 (2022)]



Work Title Meteor Detection With a New Computer Vision Approach
Open Access
  1. Yanlin Li
  2. Freddy Galindo
  3. Julio Urbina
  4. Qihou Zhou
  5. Tai Yin Huang
License In Copyright (Rights Reserved)
Work Type Article
  1. Radio Science
Publication Date October 1, 2022
Publisher Identifier (DOI)
Deposited March 13, 2023




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Work History

Version 1

  • Created
  • Added AGU_Radio_Science_20220602.pdf
  • Added Creator Yanlin Li
  • Added Creator Freddy Galindo
  • Added Creator Julio Urbina
  • Added Creator Qihou Zhou
  • Added Creator Tai Yin Huang
  • Published