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Dr.-Ing. Daniel Stromer

Researcher in the Learning Approaches for Medical Big Data Analysis (LAMBDA) group at the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg

Whenever using this database, reference the following paper:

D. Stromer, A. Vetter, H. C. Oezkan, C. Probst and A. Maier, "Enhanced Crack Segmentation (eCS): A Reference Algorithm for Segmenting Cracks in Multicrystalline Silicon Solar Cells," in IEEE Journal of Photovoltaics.
doi: 10.1109/JPHOTOV.2019.2895808

If you have any questions, feel free to contact:


eCS: Enhanced Crack Segmentation for Photovoltaics

Input Data

So far, the algorithm was exclusively tested for electroluminescence images of multicrystalline photovoltaic cells. The database consisted of 47 images (cracks and no cracks) with a size of 1024x1024 px showing a region a bit bigger than a single cell. Furthermore, we evaluated the algorithm for downsized images to decrease runtime. 


The code was tested with Python 3.5.4, skimage 0.13.0, and OpenCV 3.4.3.

Initiates file

For testing, unpack the .zip-file into your workspace and run We provide a test image as well as it's annotation in '.tif' format (8-bit, 1024 x 1024 px).




Here, the output for three exemplary input images with different parameters are presented. In the first image, no crack is present. In the second image, a small crack is present - located near a busbar. The last image shows two severe cracks, affecting the entire cell and having different orientations.



No crack
Small crack near busbar
Two large cracks