Methodology, Results, and Comparative Analysis of the Use of Mask-RCNN and YOLO Neural Network Models for the Task of Automatically Selecting Mode Tracks on Ionograms of Oblique Ionospheric Radiosonde
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Abstract
Automatic processing of ionospheric radiosonde ionograms is necessary both for operational monitoring of the ionosphere and for analyzing archived radiosonde data arrays. The central place in this task is occupied by automatic detection and classification of tracks – traces of reflected radio signal modes on ionograms. This paper presents the methodology, results, and comparative analysis of the use of neural network models (neural network architectures) for Mask RCNN and YOLO instance segmentation. The most successful models were the YOLO family, which provide both high accuracy and computational efficiency. At the same time, the exact pixel–by-pixel matching of the masks is not critical – the key requirement is the correct localization of the useful signal. The use of these models to identify tracks on ionograms has demonstrated moderate but sufficient efficiency: the main structures of the tracks are reliably identified. The most successful models were the YOLO family, which provide both high accuracy and computational efficiency. In the future, the set of models used will be expanded, primarily due to visual transformers.
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References
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