---
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](https://www.sciencedirect.com/science/article/pii/S0038092X26006328)
- **Code:** [mueez-overflow/generative-defect-isolation](https://github.com/mueez-overflow/generative-defect-isolation)
- **Original dataset:** [ucf-photovoltaics/UCF-EL-Defect](https://github.com/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.

<p align="center">
  <img src="https://raw.githubusercontent.com/mueez-overflow/generative-defect-isolation/main/assets/M0393C003000_gdi_summary.png" width="600">
</p>

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:

```bibtex
@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}
}
```