The present work will investigate an Artificial Intelligence (AI) approach in the framework of image segmentation and clustering algorithms for star sensors applications. It focuses on the architecture development and test of a Convolutional Neural Network (CNN) based algorithm and results comparison with the state of the art. The problem of image segmentation will be faced using the U-Net to detect the brightest objects in the sensor’s Field Of View (FOV) for attitude determination purposes. The dataset creation for the network training, algorithm design process and definition of performance indices are provided together with comparison test results
CONVOLUTIONAL NEURAL NETWORK APPROACH TO STAR SENSORS IMAGE PROCESSING ALGORITHMS / Mastrofini, Marco; Latorre, Francesco; Agostinelli, Ivan; Curti, Fabio. - (2021). (Intervento presentato al convegno 2021 AAS/AIAA Astrodynamics Specialist Conference tenutosi a Big Sky (Montana - USA), Vituale).
CONVOLUTIONAL NEURAL NETWORK APPROACH TO STAR SENSORS IMAGE PROCESSING ALGORITHMS
Marco Mastrofini;Francesco Latorre;Ivan Agostinelli;Fabio Curti
2021
Abstract
The present work will investigate an Artificial Intelligence (AI) approach in the framework of image segmentation and clustering algorithms for star sensors applications. It focuses on the architecture development and test of a Convolutional Neural Network (CNN) based algorithm and results comparison with the state of the art. The problem of image segmentation will be faced using the U-Net to detect the brightest objects in the sensor’s Field Of View (FOV) for attitude determination purposes. The dataset creation for the network training, algorithm design process and definition of performance indices are provided together with comparison test resultsI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.