# A scalable framework for annotating photovoltaic cell defects in electroluminescence images

arXiv: https://arxiv.org/abs/2212.07768 · HTML: https://mlsanity.com/paper/2212.07768/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning](https://mlsanity.com/paper/2110.05474/) (2021)
- [Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images](https://mlsanity.com/paper/1807.02894/) (2018)
- [Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images](https://mlsanity.com/paper/1806.06530/) (2018)
- [Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm](https://mlsanity.com/paper/2001.11248/) (2020)
- [Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules](https://mlsanity.com/paper/2106.10962/) (2021)

## Built on by (most cited first, 2 of 2)

- [Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing](https://mlsanity.com/paper/2505.01032/) (2025)
- [Edge Detection based on Channel Attention and Inter-region Independence Test](https://mlsanity.com/paper/2505.01040/) (2025)

## Abstract (arXiv)

The correct functioning of photovoltaic (PV) cells is critical to ensuring the optimal performance of a solar plant. Anomaly detection techniques for PV cells can result in significant cost savings in operation and maintenance (O&M). Recent research has focused on deep learning techniques for automatically detecting anomalies in Electroluminescence (EL) images. Automated anomaly annotations can improve current O&M methodologies and help develop decision-making systems to extend the life-cycle of the PV cells and predict failures. This paper addresses the lack of anomaly segmentation annotations in the literature by proposing a combination of state-of-the-art data-driven techniques to create a Golden Standard benchmark. The proposed method stands out for (1) its adaptability to new PV cell types, (2) cost-efficient fine-tuning, and (3) leverage public datasets to generate advanced annotations. The methodology has been validated in the annotation of a widely used dataset, obtaining a reduction of the annotation cost by 60%.