  Photovoltaic is one of the most important renewable energy sources for
dealing with world-wide steadily increasing energy consumption. This raises the
demand for fast and scalable automatic quality management during production and
operation. However, the detection and segmentation of cracks on
electroluminescence (EL) images of mono- or polycrystalline solar modules is a
challenging task. In this work, we propose a weakly supervised learning
strategy that only uses image-level annotations to obtain a method that is
capable of segmenting cracks on EL images of solar cells. We use a modified
ResNet-50 to derive a segmentation from network activation maps. We use defect
classification as a surrogate task to train the network. To this end, we apply
normalized Lp normalization to aggregate the activation maps into single scores
for classification. In addition, we provide a study how different
parameterizations of the normalized Lp layer affect the segmentation
performance. This approach shows promising results for the given task. However,
we think that the method has the potential to solve other weakly supervised
segmentation problems as well.
