This paper deals with the problem of automatically detecting contrails, investigating the possible usage of infrared and visible images, through the construction of a labeled dataset from ground-based all-sky camera images. The images are preprocessed to remove the geometrical distortion introduced by the fisheye lens of the cameras and then fed to three different models, namely Roboflow Train 3.0, YOLOv8s, and YOLOv9c. The performances of the three approaches are then compared. The results show that the models trained on visible images outperform those trained on the infrared ones, with yolov9c being the best on both types of images, followed closely by yolov8s.

Ground-Based Contrail Detection by Means of Computer Vision Models: A Comparison Between Visible and Infrared Images / Pertino, Paolo; Pavarino, Leonardo; Lomolino, Simone; Miotto, Enrico; Cambrin, Daniele Rege; Garza, Paolo; Ogliari, Emanuele. - (2024), pp. 254-259. (Intervento presentato al convegno 8th IEEE International Forum on Research and Technologies for Society and Industry Innovation, RTSI 2024 tenutosi a Milan (ITA) nel 18-20 September 2024) [10.1109/rtsi61910.2024.10761667].

Ground-Based Contrail Detection by Means of Computer Vision Models: A Comparison Between Visible and Infrared Images

Pavarino, Leonardo;Miotto, Enrico;Cambrin, Daniele Rege;Garza, Paolo;
2024

Abstract

This paper deals with the problem of automatically detecting contrails, investigating the possible usage of infrared and visible images, through the construction of a labeled dataset from ground-based all-sky camera images. The images are preprocessed to remove the geometrical distortion introduced by the fisheye lens of the cameras and then fed to three different models, namely Roboflow Train 3.0, YOLOv8s, and YOLOv9c. The performances of the three approaches are then compared. The results show that the models trained on visible images outperform those trained on the infrared ones, with yolov9c being the best on both types of images, followed closely by yolov8s.
2024
979-8-3503-6213-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3001615