Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm Photovoltaic is one of the most important renewable energy sources for dealing with worldwide 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 imagelevel annotations to obtain a method that is capable of segmenting cracks on EL images of solar cells. We use a modified ResNet50 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.

Deep Learning Based Automatic Crack Detection and Segmentation for Unmanned Aerial Vehicle Inspections Automatic crack detection and segmentation plays a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and the performance of it is compared with other state of the art network architecture. We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmarkdataset on the internet for crack detection and segmentation, which is opensourced for the research community. Our feature pyramid crack segmentation network is tested on the benchmarkdataset and gives satisfactory segmentation results. A framework for automatic unmanned aerial vehicle inspections is also proposed for the crack inspection tasks of various concrete structures.