# Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images

arXiv: https://arxiv.org/abs/1807.02894 · HTML: https://mlsanity.com/paper/1807.02894/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2018.

## Built on by (most cited first, 30 of 35)

- [Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2203.14506/) (2022)
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- [Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2310.12790/) (2023)
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- [A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images](https://mlsanity.com/paper/2204.11161/) (2022)
- [MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2211.13968/) (2022)
- [Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2502.20981/) (2025)
- [Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules](https://mlsanity.com/paper/2106.10962/) (2021)
- [Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network](https://mlsanity.com/paper/2103.03518/) (2021)
- [A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle](https://mlsanity.com/paper/2111.11709/) (2021)
- [CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection](https://mlsanity.com/paper/2204.11766/) (2022)
- [A scalable framework for annotating photovoltaic cell defects in electroluminescence images](https://mlsanity.com/paper/2212.07768/) (2022)
- [A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation](https://mlsanity.com/paper/2302.07455/) (2023)
- [Scalable Concept Extraction in Industry 4.0](https://mlsanity.com/paper/2306.03551/) (2023)
- [SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection](https://mlsanity.com/paper/2306.08366/) (2023)
- [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)
- [Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2405.16368/) (2024)
- [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)
- [Module-Power Prediction from PL Measurements using Deep Learning](https://mlsanity.com/paper/2108.13640/) (2021)
- [Application of the Singular Spectrum Analysis on electroluminescence images of thin-film photovoltaic modules](https://mlsanity.com/paper/2109.04048/) (2021)
- [Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells](https://mlsanity.com/paper/2208.05994/) (2022)
- [Semi-supervised binary classification with latent distance learning](https://mlsanity.com/paper/2211.15153/) (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)
- [PV-faultNet: Optimized CNN Architecture to detect defects resulting efficient PV production](https://mlsanity.com/paper/2411.02997/) (2024)
- [MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells](https://mlsanity.com/paper/2603.13337/) (2026)

## Abstract (arXiv)

Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects. In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1,968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a slightly lower average accuracy of 82.44%, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.