An automatic detection model for cracks in photovoltaic cells based on electroluminescence imaging using improved YOLOv7
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02724-7.pdf
Reference33 articles.
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3. Goudelis, G., Lazaridis, P.I., Dhimish, M.: A review of models for photovoltaic crack and hotspot prediction. Energies 15, 4303 (2022). https://doi.org/10.3390/en15124303
4. Sohail, A., Ul Islam, N., Ul Haq, A., Ul Islam, S., Shafi, I., Park, J.: Fault detection and computation of power in PV cells under faulty conditions using deep-learning. Energy Rep. 9, 4325–4336 (2023). https://doi.org/10.1016/j.egyr.2023.03.094
5. Bommes, L., Hoffmann, M., Buerhop-Lutz, C., Pickel, T., Hauch, J., Brabec, C., Maier, A., Marius Peters, I.: Anomaly detection in IR images of PV modules using supervised contrastive learning. Prog. Photovoltaics Res. Appl. 30, 597–614 (2022). https://doi.org/10.1002/pip.3518
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