# A Comprehensive Case Study on the Performance of Machine Learning Methods on the Classification of Solar Panel Electroluminescence Images

arXiv: https://arxiv.org/abs/2408.06229 · HTML: https://mlsanity.com/paper/2408.06229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images](https://mlsanity.com/paper/1807.02894/) (2018)
- [Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization](https://mlsanity.com/paper/2107.01131/) (2021)
- [Statistical Perspectives on Reliability of Artificial Intelligence Systems](https://mlsanity.com/paper/2111.05391/) (2021)
- [Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules](https://mlsanity.com/paper/2010.07556/) (2020)

## Built on by (most cited first, 1 of 1)

- [MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells](https://mlsanity.com/paper/2603.13337/) (2026)

## Abstract (arXiv)

Photovoltaics (PV) are widely used to harvest solar energy, an important form of renewable energy. Photovoltaic arrays consist of multiple solar panels constructed from solar cells. Solar cells in the field are vulnerable to various defects, and electroluminescence (EL) imaging provides effective and non-destructive diagnostics to detect those defects. We use multiple traditional machine learning and modern deep learning models to classify EL solar cell images into different functional/defective categories. Because of the asymmetry in the number of functional vs. defective cells, an imbalanced label problem arises in the EL image data. The current literature lacks insights on which methods and metrics to use for model training and prediction. In this paper, we comprehensively compare different machine learning and deep learning methods under different performance metrics on the classification of solar cell EL images from monocrystalline and polycrystalline modules. We provide a comprehensive discussion on different metrics. Our results provide insights and guidelines for practitioners in selecting prediction methods and performance metrics.