Research

Method for Estimation and Correction of Perspective Distortion of Electroluminescence Images of Photovoltaic Panels

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Abstract

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.