# MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells

arXiv: https://arxiv.org/abs/2603.13337 · HTML: https://mlsanity.com/paper/2603.13337/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 6 Mar 2026.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation](https://mlsanity.com/paper/1801.00926/) (2018)
- [Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images](https://mlsanity.com/paper/1807.02894/) (2018)
- [Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification](https://mlsanity.com/paper/2310.10443/) (2023)
- [Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules](https://mlsanity.com/paper/2106.10962/) (2021)
- [Instance Segmentation of Dense and Overlapping Objects via Layering](https://mlsanity.com/paper/2210.03551/) (2022)
- [Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules](https://mlsanity.com/paper/2010.07556/) (2020)
- [A Comprehensive Case Study on the Performance of Machine Learning Methods on the Classification of Solar Panel Electroluminescence Images](https://mlsanity.com/paper/2408.06229/) (2024)

## Abstract (arXiv)

Electroluminescence (EL) imaging is widely used to detect defects in photovoltaic (PV) modules, and machine learning methods have been applied to enable large-scale analysis of EL images. However, existing methods cannot assign multiple labels to the same pixel, limiting their ability to capture overlapping degradation features. We present a multi-channel U-Net architecture for pixel-level multi-label segmentation of EL images. The model outputs independent probability maps for cracks, busbars, dark areas, and non-cell regions, enabling accurate co-classification of interacting features such as cracks crossing busbars. The model achieved an accuracy of 98% and has been shown to generalize to unseen datasets. This framework offers a scalable, extensible tool for automated PV module inspection, improving defect quantification and lifetime prediction in large-scale PV systems.