TY - JOUR
T1 - Method for Estimation and Correction of Perspective Distortion of Electroluminescence Images of Photovoltaic Panels
AU - Mantel, Claire
AU - Villebro, Frederik
AU - Parikh, Harsh Rajesh
AU - Spataru, Sergiu
AU - Benatto, Gisele Alves dos Reis
AU - Sera, Dezso
AU - Poulsen, Peter Behrensdorff
AU - Forchhammer, Søren
PY - 2020
Y1 - 2020
N2 - The number of photovoltaic panels installed globally is continuously growing, requiring an automatic inspection procedure for operation and maintenance. Drones can be a useful tool to this aim as they enable fast acquisition of various imaging modalities: visual, infrared, or electroluminescence (EL). Image distortions due to perspective must be corrected to allow further automatic processing. It can be done by estimating the corresponding rotation angles to control the camera gimbal or as postprocessing to rectify the images. This article presents two methods to achieve both goals by identifying known points in the acquired image. The first method detects the four panel corners, whereas the second method finds the corners of each cell. The performance evaluation is performed first quantitatively on a validation dataset composed of 113 EL images and their corresponding ground-truth orientations. A qualitative evaluation shows satisfying performance of the rectification similarly for both methods. The quantitative performance is varying for each rotation axis. The average absolute error is 2.78 ∘ along the x -axis, 2.64 ∘ along the y -axis, and 1.28 ∘ along the z -axis for the panel method and 3.26 ∘ , 2.05 ∘ , and 1.24 ∘ for the cell method. As a proof of concept, a final test on drone-acquired EL images shows good performance for the image rectification in real-life conditions.
AB - The number of photovoltaic panels installed globally is continuously growing, requiring an automatic inspection procedure for operation and maintenance. Drones can be a useful tool to this aim as they enable fast acquisition of various imaging modalities: visual, infrared, or electroluminescence (EL). Image distortions due to perspective must be corrected to allow further automatic processing. It can be done by estimating the corresponding rotation angles to control the camera gimbal or as postprocessing to rectify the images. This article presents two methods to achieve both goals by identifying known points in the acquired image. The first method detects the four panel corners, whereas the second method finds the corners of each cell. The performance evaluation is performed first quantitatively on a validation dataset composed of 113 EL images and their corresponding ground-truth orientations. A qualitative evaluation shows satisfying performance of the rectification similarly for both methods. The quantitative performance is varying for each rotation axis. The average absolute error is 2.78 ∘ along the x -axis, 2.64 ∘ along the y -axis, and 1.28 ∘ along the z -axis for the panel method and 3.26 ∘ , 2.05 ∘ , and 1.24 ∘ for the cell method. As a proof of concept, a final test on drone-acquired EL images shows good performance for the image rectification in real-life conditions.
KW - Angle estimation
KW - Corner detection
KW - Electroluminescence (EL)
KW - Homography
KW - Imaging
KW - Perspective correction
KW - Perspective distortion
U2 - 10.1109/JPHOTOV.2020.3019949
DO - 10.1109/JPHOTOV.2020.3019949
M3 - Journal article
SN - 2156-3381
VL - 10
SP - 1797
EP - 1802
JO - IEEE Journal of Photovoltaics
JF - IEEE Journal of Photovoltaics
IS - 6
ER -