Deep machine learning for meteor monitoring : Advances with transfer learning and gradient-weighted class activation mapping
Peña-Asensio, Eloy (Universitat Autònoma de Barcelona. Departament de Química)
Trigo Rodríguez, Josep Maria (Institut de Ciències de l'Espai)
Grèbol-Tomàs, Pau (Institut de Ciències de l'Espai)
Regordosa-Avellana, David (Spanish Meteor Network)
Rimola Gibert, Albert (Universitat Autònoma de Barcelona. Departament de Química)

Fecha:	2023
Resumen:	In recent decades, the use of optical detection systems for meteor studies has increased dramatically, resulting in huge amounts of data being analyzed. Automated meteor detection tools are essential for studying the continuous meteoroid incoming flux, recovering fresh meteorites, and achieving a better understanding of our Solar System. Concerning meteor detection, distinguishing false positives between meteor and non-meteor images has traditionally been performed by hand, which is significantly time-consuming. To address this issue, we developed a fully automated pipeline that uses Convolutional Neural Networks (CNNs) to classify candidate meteor detections. Our new method is able to detect meteors even in images that contain static elements such as clouds, the Moon, and buildings. To accurately locate the meteor within each frame, we employ the Gradient-weighted Class Activation Mapping (Grad-CAM) technique. This method facilitates the identification of the region of interest by multiplying the activations from the last convolutional layer with the average of the gradients across the feature map of that layer. By combining these findings with the activation map derived from the first convolutional layer, we effectively pinpoint the most probable pixel location of the meteor. We trained and evaluated our model on a large dataset collected by the Spanish Meteor Network (SPMN) and achieved a precision of 98%. Our new methodology presented here has the potential to reduce the workload of meteor scientists and station operators and improve the accuracy of meteor tracking and classification.
Ayudas:	Agencia Estatal de Investigación CEX2020-001058-M European Commission 865657 Agencia Estatal de Investigación PID2021-128062NB-I00 Agencia Estatal de Investigación PID2021-126427NB-I00
Nota:	Altres ajuts: acords transformatius de la UAB
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Meteorites ; Meteors ; Meteoroids ; Machine learning ; Convolutional neural networks ; Transfer learning
Publicado en:	Planetary and Space Science, Vol. 238 (November 2023) , art. 105802, ISSN 0032-0633

DOI: 10.1016/j.pss.2023.105802

9 p, 1.5 MB

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