An automated method for image classification by selecting important regions applied to a high-resolution meteor imagery dataset: a path to real-time meteor classification.

As part of the PRISMA project, we aim to create a neural network for detecting transient atmospheric events caused by meteoroids entering the atmosphere. The current approach to detection is through motion detection using the open source software FRIPON / Freeture. However, this approach is inefficient as this technology is not selective and generates different events (detection) than the ones it should monitor, leading to false positives. The project proposed by N3 in collaboration with the National Institute of Astrophysics in Turin provides for the development and training of a neural network that detects in real time the entry of meteoroids into the atmosphere. The neural network, trained on a dataset of images created starting from a set of video recordings of the events, will be designed in Python and tested with Google Colab, a service provided by Google that allows users to take advantage of the flexibility of a hosted Jupyter notebook and provides free access to computing resources, including GPUs. PRISMA (First Italian Network for Systematic Surveillance of Meteors and Atmosphere) is a collaborative project proposed and coordinated by the National Institute of Astrophysics (INAF), with the participation of research institutes, associations and schools. The full list of participants can be found on the website www.prisma.inaf.it.