STC-Net: Electroluminescence-Based Solar Cell Crack Segmentation for Power Loss Estimation
2026-08-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a method called STC-Net to better find cracks in pictures of solar panels. Their system uses special clues about edges and shapes to detect thin, long cracks more accurately and keep their boundaries clear. They also show how this crack detection can help estimate how much power the solar panel might lose. Tests show their method works well both on known data and new unseen images, linking image analysis directly to solar panel health.
electroluminescence imagingphotovoltaic cellscrack segmentationimage segmentationMIoUMDicepower-loss estimationboundary refinementsolar panel defectsinactive-area proxy
Authors
Shanaka Ramesh Gunasekara, Akila Eranda Devanarayana, Imasha Guruge, Nuwantha Fernando, Ehsan Asadi
Abstract
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.