# Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images

arXiv: https://arxiv.org/abs/1806.06530 · HTML: https://mlsanity.com/paper/1806.06530/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2018.

## Built on by (most cited first, 9 of 9)

- [A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images](https://mlsanity.com/paper/2204.11161/) (2022)
- [PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation](https://mlsanity.com/paper/2203.14457/) (2022)
- [SSGD: A smartphone screen glass dataset for defect detection](https://mlsanity.com/paper/2303.06673/) (2023)
- [A scalable framework for annotating photovoltaic cell defects in electroluminescence images](https://mlsanity.com/paper/2212.07768/) (2022)
- [A Large-scale Evaluation of Pretraining Paradigms for the Detection of Defects in Electroluminescence Solar Cell Images](https://mlsanity.com/paper/2402.17611/) (2024)
- [Advancing Automatic Photovoltaic Defect Detection using Semi-Supervised Semantic Segmentation of Electroluminescence Images](https://mlsanity.com/paper/2404.13693/) (2024)
- [A Light in the Dark: Deep Learning Practices for Industrial Computer Vision](https://mlsanity.com/paper/2201.02028/) (2022)
- [Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer Vision](https://mlsanity.com/paper/2403.05694/) (2024)
- [Machine learning approaches for automatic defect detection in photovoltaic systems](https://mlsanity.com/paper/2409.16069/) (2024)

## Abstract (arXiv)

High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kinds of defects, which is time-consuming and expensive. Automated segmentation of cells is therefore a key step in automating the visual inspection workflow. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. This enables controlled studies on large amounts of data to understanding the effects of module degradation over time-a process not yet fully understood. The proposed method infers in several steps a high-level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 94.47% and an F1 score of 97.62%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks.