Abstract
Abstract. This study describes some tests carried out, within the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of ice), for the geometrical ice detection on airplane wings. The purpose of these analysis is to estimate thickness and shape of the ice that an RGB sensor is able to detect on large aircrafts as Boeing 737-800. However, field testing are not available yet, therefore, in order to simulate the final configuration, a steel panel has been used to reproduce the aircraft surface. The adopted methodology consists in defining a reference surface and modelling its 3D shape with and without ice through photogrammetric acquisitions collected by a DJI Mavic Air drone hosting a RGB camera and processed by Agisoft Metashape software. The comparison among models with and without the ice has been presented and results show that it is possible to identify the ice, even though some noise still remains due to the geometric reconstruction itself. Finally, using 3dReshaper and Matlab software, the authors develop various analysis defining the operative limits, the processing time, the correct setting up of Metashape for a more accurate ice detection, the optimization of the methodology in terms of processing time, precision and completeness. The procedure can certainly be more reliable considering the usage of the hyperspectral sensor technique as future implementation.
Highlights
In the aircraft industry, ice accumulation on lifting or control surfaces of an airplane represents one of the central aspects of the flight performance reduction
The SEI (Spectral Evidence of Ice) (Falcone et al, 2019) project proposes to provide a solution that includes experiments about spectral sensor fusion techniques of data acquired by Autonomous Aerial Vehicles (AAVs) during autonomous aircraft pre-flight inspection without the intervention of expert operators
If no initial External Orientation Parameters (EOPs) were provided (for example from the Global Navigation Satellite Systems (GNSS) data collected during the flight), the introduction of the coordinates of Ground Control Points (GCPs) is, here, required to precisely georeference the model in the selected reference coordinate system
Summary
Ice accumulation on lifting or control surfaces of an airplane represents one of the central aspects of the flight performance reduction. The SEI (Spectral Evidence of Ice) (Falcone et al, 2019) project proposes to provide a solution that includes experiments about spectral sensor fusion techniques of data acquired by Autonomous Aerial Vehicles (AAVs) during autonomous aircraft pre-flight inspection without the intervention of expert operators. The identification of the areas covered by ice would allow to reduce the high amount of de-icing liquid usually involved in the process, so it would be possible to minimize the impact on the environment. Another benefit of the proposed method is a significant reduction in defrosting time, depending on aircraft size, extent of frost coverage and ambient conditions. Most used devices were those able to measuring signals as radio frequency (Abaunza, Donnangelo, 1998), This contribution has been peer-reviewed
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