mlsanityData to 13 Oct 2026

Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images

arXiv 1807.02894 ↗First posted 8 Jul 2018cs.CVcited by 35

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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.

Key facts. Cited by 35 papers in the map (data through 13 Oct 2026). First posted on arXiv 8 Jul 2018.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 6; 2022: 8; 2023: 4; 2024: 9; 2025: 2; 2026: 6.

35 papers.

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  1. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  2. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 89
  3. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  4. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  5. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  6. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  7. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  8. 2022A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Imagescited by 21
  9. 2022MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detectioncited by 19
  10. 2025Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detectioncited by 10
  11. 2021Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modulescited by 5
  12. 2021Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Networkcited by 4
23 more
  1. 2021A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehiclecited by 3
  2. 2022CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspectioncited by 2
  3. 2022A scalable framework for annotating photovoltaic cell defects in electroluminescence imagescited by 2
  4. 2023A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillationcited by 2
  5. 2023Scalable Concept Extraction in Industry 4.0cited by 2
  6. 2023SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detectioncited by 2
  7. 2024A Large-scale Evaluation of Pretraining Paradigms for the Detection of Defects in Electroluminescence Solar Cell Imagescited by 1
  8. 2024Advancing Automatic Photovoltaic Defect Detection using Semi-Supervised Semantic Segmentation of Electroluminescence Imagescited by 1
  9. 2024Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detectioncited by 1
  10. 2024A Comprehensive Case Study on the Performance of Machine Learning Methods on the Classification of Solar Panel Electroluminescence Imagescited by 1
  11. 2021Module-Power Prediction from PL Measurements using Deep Learningcited by 0
  12. 2021Application of the Singular Spectrum Analysis on electroluminescence images of thin-film photovoltaic modulescited by 0
  13. 2022Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cellscited by 0
  14. 2022Semi-supervised binary classification with latent distance learningcited by 0
  15. 2024Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer Visioncited by 0
  16. 2024Machine learning approaches for automatic defect detection in photovoltaic systemscited by 0
  17. 2024PV-faultNet: Optimized CNN Architecture to detect defects resulting efficient PV productioncited by 0
  18. 2026MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cellscited by 0
  19. 2026A Self-Evolving Defect Detection Framework for Industrial Photovoltaic Systemscited by 0
  20. 2026Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detectioncited by 0
  21. 2026Joint-Embedding Predictive Architecture for Solar PV Panel Fault Classificationcited by 0
  22. 2026STC-Net: Electroluminescence-Based Solar Cell Crack Segmentation for Power Loss Estimationcited by 0
  23. 2026A Generative Approach for Improving Multi-Label Defect Classification in Photovoltaic Modulescited by 0