The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 183–189, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-183-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 183–189, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-183-2020

  12 Aug 2020

12 Aug 2020

ICE DETECTION ON AIRPLANE WINGS USING A PHOTOGRAMMETRIC POINT CLOUD: A SIMULATION

I. Aicardi1,2, A. Lingua1,2, L. Mazzara1,2, M. A. Musci1,2, and G. Rizzo1 I. Aicardi et al.
  • 1DIATI, Politecnico di Torino, Torino, Italy
  • 2PIC4SeR, Politecnico di Torino Interdepartmental Centre for Service Robotics, Torino, Italy

Keywords: Ice Detection, Aerial Photogrammetry, Autonomous Aerial Vehicle, Dense Point Cloud, RGB Camera, Structure from Motion

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.