In this paper, a computer vision approach applied to PV plant for identification of the panels and thermal anomalies detection has been proposed. The approach integrates geographic information gathered from GNSS with results of a computer vision template matching algorithm applied to thermal images. This combination allows to perform panel identification by assigning to each module an identifier that remains consistent across different flight sessions.Through the proposed matching algorithm different templates have been exploited, to detect both panel extension and the presence of defects.The proposed approach opens up to further work to be focused mainly on the following points, as already discussed in [3]: a barometric altimeter to be tested in order to assess the RPAS altimeter resolution; a UAV/RPAS ground speed of less than 3 m/s; a gimbal orientation normal to object reference system; the evaluation of the misalignments (few milliseconds) introduced by wind gusts; assessment of the PV panels in the case of lack of information about PV panels placement in a planar surface; inclination of the PV panels that reduces their cross-section as seen from the UAV/RPAS; introduction of sensor lens distortion that has to be assessed and mitigated during the tests, since it accounts for one of the most significant error source budget; error in position resolution obtained from the rover on-board of GNSS receiver.

A UAV System for Photovoltaic Plants Inspection

M. L. Bernardi;Ullo S.
2018-01-01

Abstract

In this paper, a computer vision approach applied to PV plant for identification of the panels and thermal anomalies detection has been proposed. The approach integrates geographic information gathered from GNSS with results of a computer vision template matching algorithm applied to thermal images. This combination allows to perform panel identification by assigning to each module an identifier that remains consistent across different flight sessions.Through the proposed matching algorithm different templates have been exploited, to detect both panel extension and the presence of defects.The proposed approach opens up to further work to be focused mainly on the following points, as already discussed in [3]: a barometric altimeter to be tested in order to assess the RPAS altimeter resolution; a UAV/RPAS ground speed of less than 3 m/s; a gimbal orientation normal to object reference system; the evaluation of the misalignments (few milliseconds) introduced by wind gusts; assessment of the PV panels in the case of lack of information about PV panels placement in a planar surface; inclination of the PV panels that reduces their cross-section as seen from the UAV/RPAS; introduction of sensor lens distortion that has to be assessed and mitigated during the tests, since it accounts for one of the most significant error source budget; error in position resolution obtained from the rover on-board of GNSS receiver.
2018
Unmanned Aerial Vehicles (UAVs); Photovoltaic (PV) Plants Inspection ; Defect Inspection
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/6710
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