# STC-Net: Electroluminescence-Based Solar Cell Crack Segmentation for Power Loss Estimation

arXiv: https://arxiv.org/abs/2608.01714 · HTML: https://mlsanity.com/paper/2608.01714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images](https://mlsanity.com/paper/1807.02894/) (2018)
- [Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules](https://mlsanity.com/paper/2106.10962/) (2021)
- [Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules](https://mlsanity.com/paper/2010.07556/) (2020)

## Abstract (arXiv)

Accurate crack assessment in electroluminescence (EL) images is important for photovoltaic (PV) reliability analysis, yet existing segmentation methods often fail to capture the thin, elongated, and structurally constrained nature of crack defects. This paper proposes a Solar Topology Crack Network (STC-Net) that incorporates edge priors, spectral priors, and a boundary-topology refinement module to improve crack continuity and boundary preservation. The framework further extends segmentation to power-loss estimation by deriving a crack-associated inactive-area proxy from the predicted masks. Experiments on the PVEL-S dataset show that STC-Net achieves 95.98 MIoU, 98.01 MDice, and 98.00 MAcc during training, and 72.52 MIoU and 80.16 MDice on unseen test samples. These results demonstrate that STC-Net provides accurate crack localization while offering a practical link between EL-based defect segmentation and PV degradation assessment.