Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells

Ojas Sanghi, Norman Jost, Benjamin G. Pierce, Emma Cooper, Isaiah H. Deane, Jennifer L. Braid

Abstract

Electroluminescence (EL) imaging is widely used to detect defects in photovoltaic (PV) modules, and machine learning methods have been applied to enable large-scale analysis of EL images. However, existing methods cannot assign multiple labels to the same pixel, limiting their ability to capture overlapping degradation features. We present a multi-channel U-Net architecture for pixel-level multi-label segmentation of EL images. The model outputs independent probability maps for cracks, busbars, dark areas, and non-cell regions, enabling accurate co-classification of interacting features such as cracks crossing busbars. The model achieved an accuracy of 98% and has been shown to generalize to unseen datasets. This framework offers a scalable, extensible tool for automated PV module inspection, improving defect quantification and lifetime prediction in large-scale PV systems.

MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells

Abstract

Electroluminescence (EL) imaging is widely used to detect defects in photovoltaic (PV) modules, and machine learning methods have been applied to enable large-scale analysis of EL images. However, existing methods cannot assign multiple labels to the same pixel, limiting their ability to capture overlapping degradation features. We present a multi-channel U-Net architecture for pixel-level multi-label segmentation of EL images. The model outputs independent probability maps for cracks, busbars, dark areas, and non-cell regions, enabling accurate co-classification of interacting features such as cracks crossing busbars. The model achieved an accuracy of 98% and has been shown to generalize to unseen datasets. This framework offers a scalable, extensible tool for automated PV module inspection, improving defect quantification and lifetime prediction in large-scale PV systems.
Paper Structure (15 sections, 10 figures, 4 tables)

This paper contains 15 sections, 10 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Related Work
  4. Our Work
  5. Methods
  6. Multi-Channeled Masks and Predictions
  7. Cross-Validation and Hyperparameter Optimization
  8. Dataset
  9. Results
  10. Discussion
  11. Model Applicability and Extensibility
  12. Alternative Strategies to Multi-Classification
  13. Future Work
  14. Conclusion
  15. Acknowledgments

Figures (10)

  • Figure 1: High-level overview of model flow. Made with iqbalHarisIqbal88PlotNeuralNetV1002018. Yellow/orange blocks denote convolutional feature maps at each resolution, with block width indicating the number of channels and block height indicating spatial resolution. Darker orange/red inner regions highlight the activated feature representations after convolution and ReLU. Blue-tinted blocks in the decoder indicate upsampled feature maps produced by transposed convolutions. Semi-transparent blocks represent feature concatenation via skip connections between corresponding encoder and decoder stages. The final purple block denotes the output layer with per-channel sigmoid activations, producing independent probability maps for each defect class. Arrows indicate the forward flow of information, while long horizontal connections visualize encoder-decoder skip connections.
  • Figure 2: High accuracy results from our model
  • Figure 3: Predictions of MultiSolSegment (MSS) compared to PV-Vision (PVV)
  • Figure 4: Metrics of MultiSolSegment compared to PV-Vision
  • Figure 5: Quantitative detected crack count of MultiSolSegment compared to PV-Vision
  • ...and 5 more figures