Datasets:
pretty_name: UCF-EL-GDI
tags:
- photovoltaics
- electroluminescence
- synthetic-data
- inpainting
- defect-classification
UCF-EL-GDI
UCF-EL-GDI is a collection of synthetic photovoltaic electroluminescence (EL) images generated from the public UCF-EL-Defect dataset using Generative Defect Isolation (GDI).
GDI uses the original segmentation annotations to remove selected defects through neural inpainting, producing isolated single-defect samples as well as samples with no defect. For this dataset, only images containing at least one defect with an annotated area greater than 10% of the total image area were selected. This threshold is user-configurable in the generation code.
Full methodology, generation instructions, and implementation details are available in the project repository.
Links
- Paper: A Generative Approach for Improving Multi-Label Defect Classification in Photovoltaic Modules
- Code: mueez-overflow/generative-defect-isolation
- Original dataset: ucf-photovoltaics/UCF-EL-Defect
Example
The example below illustrates the GDI process, showing the original defect annotations alongside the generated single-defect and no-defect samples.
In this example, the source image contains four annotated defect classes. Of these, only Contact_NearSolderPad and Crack_Resistive satisfy the configured area threshold, resulting in isolated samples for these two defect classes along with a No_Defect sample.
Citation
If you use GDI, the generated synthetic data, or any part of this work, please cite:
@article{mueez2026gdi,
title = {A generative approach for improving multi-label defect classification in photovoltaic modules},
journal = {Solar Energy},
volume = {317},
pages = {114943},
year = {2026},
issn = {0038-092X},
doi = {10.1016/j.solener.2026.114943},
author = {Abdul Mueez and Yogesh S. Rawat and Shruti Vyas}
}