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Fast, non-destructive and on-site quality control tools, mainly highsensitive imaging techniques, are important to assess the reliability ofphotovoltaic plants. To minimize the risk of further damages and electricalyield losses, electroluminescence (EL) imaging is used to detect local defectsin an early stage, which might cause future electric losses. For an automateddefect recognition on EL measurements, a robust detection and rectification ofmodules, as well as an optional segmentation into cells is required. This paperintroduces a method to detect solar modules and crossing points between solarcells in EL images. We only require 1-D image statistics for the detection,resulting in an approach that is computationally efficient. In addition, themethod is able to detect the modules under perspective distortion and inscenarios, where multiple modules are visible in the image. We compare ourmethod to the state of the art and show that it is superior in presence ofperspective distortion while the performance on images, where the module isroughly coplanar to the detector, is similar to the reference method. Finally,we show that we greatly improve in terms of computational time in comparison tothe reference method.

Fast and robust detection of solar modules in electroluminescence images