NEARBY Platform for Automatic Asteroids Detection and EURONEAR Surveys

Gorgan, Dorian; Vaduvescu, Ovidiu; Stefanut, Teodor; Bacu, Victor; Sabou, Adrian; Balazs, Denisa Copandean; Nandra, Constantin; Boldea, Costin; Boldea, Afrodita; Predatu, Marian; Pinter, Viktoria; Stanica, Adrian

doi:10.48550/arxiv.1903.03479

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…In the field of astronomy, researchers are capturing various sets of images using different telescopes. Most of the time, the data are obtained after repeated sessions of observations of areas of the sky (fields), which can lead to the accumulation of large volumes of data that need to be analyzed in order to extract useful information [7]. To make effective use of such data, classification is one of the most widely seen applications of machine learning techniques, for example, in [8].…”

Section: Related Workmentioning

confidence: 99%

Assessment of Asteroid Classification Using Deep Convolutional Neural Networks

Bacu,

Nandra,

Sabou

et al. 2023

Aerospace

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Near-Earth Asteroids represent potential threats to human life because their trajectories may bring them in the proximity of the Earth. Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass in the Earth’s vicinity. Additionally, there is also the problem of distinguishing asteroids from other objects in the night sky, which implies sifting through large sets of telescope image data. Within this context, we believe that employing machine learning techniques could greatly improve the detection process by sorting out the most likely asteroid candidates to be reviewed by human experts. At the moment, the use of machine learning techniques is still limited in the field of astronomy and the main goal of the present paper is to study the effectiveness of deep convolutional neural networks for the classification of astronomical objects, asteroids in this particular case, by comparing some of the well-known deep convolutional neural networks, including InceptionV3, Xception, InceptionResNetV2 and ResNet152V2. We applied transfer learning and fine-tuning on these pre-existing deep convolutional networks, and from the results that we obtained, the potential of using deep convolutional neural networks in the process of asteroid classification can be seen. The InceptionV3 model has the best results in the asteroid class, meaning that by using it, we lose the least number of valid asteroids.

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Section: Related Workmentioning

confidence: 99%

Assessment of Asteroid Classification Using Deep Convolutional Neural Networks

Bacu,

Nandra,

Sabou

et al. 2023

Aerospace

View full text Add to dashboard Cite

show abstract

Assessment of asteroid classification using deep convolutional neural networks

Bacu¹,

Nandra²,

Sabou³

et al. 2023

Preprint

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Near Earth Asteroids represent potential threats to human life because their trajectories may bring them in the proximity of the Earth. Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass through the Earth’s vicinity. Additionally, there is also the problem of distinguishing asteroids from other objects in the night sky, which implies sifting through large sets of telescope image data. Within this context, we believe that employing machine learning techniques could greatly improve the detection process by sorting out the most likely asteroid candidates to be reviewed by human experts. At the moment, the use of machine learning techniques is still limited in the field of astronomy and the main goal of the present paper is to study the effectiveness of deep CNNs for the classification of astronomical objects, asteroids in this particular case, by comparing some of the well-known deep convolutional neural networks, including InceptionV3, Xception, InceptionResNetV2 and ResNet152V2. We have applied transfer learning and fine-tuning on these pre-existing deep convolutional networks and from the results that we have obtained one can see the potential of using deep convolutional neural networks in the process of asteroid classification. The InceptionV3 model has the best results in the asteroid class, meaning that by using it, we loose the least number of valid asteroids.

show abstract

NEARBY Platform for Automatic Asteroids Detection and EURONEAR Surveys

Cited by 2 publications

References 23 publications

Assessment of Asteroid Classification Using Deep Convolutional Neural Networks

Assessment of Asteroid Classification Using Deep Convolutional Neural Networks

Assessment of asteroid classification using deep convolutional neural networks

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