Meteor Detection Using Deep Convolutional Neural Networks
Authors: Thiago César Marsola and Ana Carolina Lorena
This paper describes an application of a pre-trained Deep Convolutional Neural Network to detect meteors from night sky images. The dataset is relatively small, composed of labeled images of meteors and non-meteors from the night sky. Techniques like data augmentation were used to create data artificially, and a dropout layer was introduced to prevent overfitting at the training augmented dataset. The performance obtained by the methods using traditional five-fold cross-validation along with changing of the images for black and white, compared with the ones obtained using only the training and validation partitions and RGB colors, achieved a higher mean accuracy of 84.35%.
Example of a meteor image.