Sorry, you need to enable JavaScript to visit this website.

WEAKLY SUPERVISED SEGMENTATION OF CRACKS ON SOLAR CELLS USING NORMALIZED LP NORM

Citation Author(s):
Martin Mayr, Mathis Hoffmann, Andreas Maier, Vincent Christlein
Submitted by:
Martin Mayr
Last updated:
19 September 2019 - 8:15am
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Martin Mayr
Paper Code:
1861
 

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.

up
0 users have voted: