Automatic Crack Segmentation and Feature Extraction in Electroluminescence Images of Solar Modules

Xin Chen, Todd Karin, Cara Libby, Michael Deceglie, Peter Hacke, Timothy Silverman, Anubhav Jain

Research output: Contribution to journalArticlepeer-review

2 Scopus Citations


The effect of cracks in solar cells on the long-term degradation of photovoltaic (PV) modules remains to be determined. To investigate this effect in future studies, it is necessary to quantitatively describe the crack features (e.g., length) and correlate them with module power loss. Electroluminescence (EL) imaging is a common technique for identifying cracks. However, it is currently challenging and time-consuming to identify cracks in a large number of EL images and quantify complex crack features by human inspection. This article introduces a fast semantic segmentation method (∼0.18 s/cell) to automatically segment cracks from EL images and algorithms to extract crack features. We fine-tuned a UNet neural network model using pretrained VGG16 as the encoder and obtained an average F1 score of 0.875 and an intersection over union score of 0.782 on the testing set. With cracks and busbars segmented, we developed algorithms for extracting crack features, including the crack-isolated area, the brightness inside the isolated area, and the crack length. We also developed an automatic preprocessing tool for cropping individual cell images from EL images of PV modules (∼0.72 s/module). Our codes are published as open-source an software, and our annotated dataset composed of various types of cells is published as a benchmark for crack segmentation in EL images.

Original languageAmerican English
Pages (from-to)334-342
Number of pages9
JournalIEEE Journal of Photovoltaics
Issue number3
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2011-2012 IEEE.

NREL Publication Number

  • NREL/JA-5K00-83053


  • Crack feature extraction
  • deep learning
  • photovoltaic (PV) module
  • semantic segmentation


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