Abstract
The surveillance of meteors is important due to the possibility of studying the Universe and identifying hazardous events. The EXOSS initiative monitors the Brazilian sky with cameras in order to identify meteors, leading to a great quantity of non-meteor captures that must be filtered. We approach the task of automatically distinguishing between meteor and non-meteor images with the use of pre-trained convolutional neural networks. Our main contributions are the revision of the methodology for evaluating models on this task, showing that the previous methodology leads to an overestimation of the expected performance for future data on our dataset; and the application of probability calibration in order to improve the selection of most confident predictions, showing that apart from obtaining probabilities that better reflect the confidence of the model, calibration can lead to concrete improvements on both accuracy and coverage. Our method achieves 98% accuracy predicting on 60% of the images, improving upon the performance of the uncalibrated model of 94% accuracy predicting on 70% of the images.
Supported by FAPESP (2018/20508-2).
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Acknowledgment
We thank FAPESP for funding this research (grant 2018/20508-2), the EXOSS organization for providing the data and astronomy expertise, and Pete Gural for discussions.
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Galindo, Y., De Cicco, M., Quiles, M.G., Lorena, A.C. (2020). Monitoring Night Skies with Deep Learning. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_53
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