With the recent rise of renewable energy, usage of solar energy has also grown rapidly. Detecting faulty panels inproduction and on-site therefore has become more important. Prior works focus on fault detection using the e.g. the current, voltage and temperature of solar modules as inputs [6, 1], but the localization of defects using imaging and machine learning has only recently gained attention [5, 4].
This work studies the detection of defects in electroluminescence (EL) images of solar cells using state of the art computer vision techniques with a focus on crack detection. Previously, in order to train a model to predict pixel classifications, exhaustive labelling of every pixel in an image of the dataset was required. State of the art training methods allow models to predict coarse segmentations using only image-wise classification labels by means of weakly supervised training. Recently, it has been shown that these methods can be applied to perform a coarse segmentation of cracks on EL images of solar cells as well .
This thesis aims to improve upon the existing method. To this end, weakly supervised learning methods like guided backpropagation, grad-cam, score-cam and adversarial learning [5, 9, 2, 7, 8, 3] will be implemented to train a model that reliably and accurately localizes cracks in a dataset of about 40k image-wise annotated EL images of solar cells. Finally, a thorough evaluation will show, if these methods can improve over the state of the art.
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