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Photovoltaic is one of the most important renewable energy sources fordealing with world-wide steadily increasing energy consumption. This raises thedemand for fast and scalable automatic quality management during production andoperation. However, the detection and segmentation of cracks onelectroluminescence (EL) images of mono- or polycrystalline solar modules is achallenging task. In this work, we propose a weakly supervised learningstrategy that only uses image-level annotations to obtain a method that iscapable of segmenting cracks on EL images of solar cells. We use a modifiedResNet-50 to derive a segmentation from network activation maps. We use defectclassification as a surrogate task to train the network. To this end, we applynormalized Lp normalization to aggregate the activation maps into single scoresfor classification. In addition, we provide a study how differentparameterizations of the normalized Lp layer affect the segmentationperformance. This approach shows promising results for the given task. However,we think that the method has the potential to solve other weakly supervisedsegmentation problems as well.

Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm