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Meteor detection and localization using YOLOv3 and YOLOv4

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Abstract

Meteors in the United Arab Emirates are observed daily through the U.A.E. Meteor Monitoring Network (UAEMMN). As of September 2022, more than 40,000 meteors have been observed. However, the high sensitivity of the network also captures non-meteor objects such as airplanes, birds, insects, and space debris appearing in the atmosphere. To accurately identify and label meteors, this study employs object detection algorithms to reduce data and accurately detect meteor and non-meteor objects. The YOLOv3 and YOLOv4 object detection algorithms, utilizing convolutional neural networks, were utilized in this research. The models were trained on both an imbalanced and a balanced dataset that consisted of thousands of images. The imbalanced YOLOv4 model yielded the highest recall score of 98.5% followed by the imbalanced YOLOv3 model with a recall score of 98%. The highest accuracy result was also achieved by the imbalanced YOLOv4 model, with a score of 90%. Overall, all the four models were successful at labeling meteors with a confidence more than 95%. The proposed study represents a significant contribution to the field of meteor-related image analysis using low-cost cameras and machine learning. It also holds promising implications for further research and development in this area.

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Data availability

All of the images used to build the dataset are obtained from the UAE Meteor Monitoring Network unit at the Sharjah Academy for Astronomy, Space Sciences, and Technology. For acquiring data, please contact the corresponding author at aalowais@sharjah.ac.ae.

Notes

  1. https://sonotaco.com/.

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Authors and Affiliations

  1. Sharjah Academy for Astronomy, Space Sciences, and Technology, University of Sharjah, POB 27272, Sharjah, UAE

    Aisha Al-Owais, Maryam E. Sharif, Sarra Ghali, Maha Abu Serdaneh, Omar Belal & Ilias Fernini

  2. Department of Applied Physics and Astronomy, University of Sharjah, POB 27272, Sharjah, UAE

    Ilias Fernini

Authors
  1. Aisha Al-Owais
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  2. Maryam E. Sharif
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  3. Sarra Ghali
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  4. Maha Abu Serdaneh
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  5. Omar Belal
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  6. Ilias Fernini
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Contributions

AA performed model training and wrote the article. She analyzed the models’ performance and interpreted the prediction results. MS collected data, separated them into the respective classes, and cross-validated them after labeling. She provided testing data and contributed to interpreting the results. SG constructed the table concerning previous studies. She contributed to data labeling and initial model training. MAS contributed to data labeling and initial model training. OB contributed to data labeling and initial model training. IF edited the article in terms of content and English language.

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Correspondence to Aisha Al-Owais.

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Al-Owais, A., Sharif, M.E., Ghali, S. et al. Meteor detection and localization using YOLOv3 and YOLOv4. Neural Comput & Applic 35, 15709–15720 (2023). https://doi.org/10.1007/s00521-023-08575-0

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  • DOI: https://doi.org/10.1007/s00521-023-08575-0

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