Open Access
| Issue |
BIO Web Conf.
Volume 212, 2026
1st International Conference on Environment, Energy, and Materials for Sustainable Development (IC2EM-SDT’25)
|
|
|---|---|---|
| Article Number | 01030 | |
| Number of page(s) | 5 | |
| DOI | https://doi.org/10.1051/bioconf/202621201030 | |
| Published online | 23 January 2026 | |
- M. A. Green, E. D. Dunlop, J. Hohl-Ebinger, M. Yoshita, N. Kopidakis, and X. Hao, ‘solar cell efficiency tables (version 56)’, prog. photovolt. res. appl., vol. 28, no. 7, pp. 629–638, 2020, doi: 10.1002/pip.3303. [Google Scholar]
- A. Anctil, C. W. Babbitt, R. P. Raffaelle, and B. J. Landi, ‘cumulative energy demand for small molecule and polymer photovoltaics’, prog. photovolt. res. appl., vol. 21, no. 7, pp. 1541–1554, nov. 2013, doi: 10.1002/pip.2226. [Google Scholar]
- Y. Wang et al., ‘adaptive automatic solar cell defect detection and classification based on absolute electroluminescence imaging’, energy, vol. 229, p. 120606, aug. 2021, doi: 10.1016/j.energy.2021.120606. [Google Scholar]
- W. Tang, Q. Yang, K. Xiong, and W. Yan, ‘deep learning based automatic defect identification of photovoltaic module using electroluminescence images’, sol. energy, vol. 201, pp. 453–460, may 2020, doi: 10.1016/j.solener.2020.03.049. [Google Scholar]
- M. Y. Demirci, N. Beşli, and A. Gümüşçü, ‘efficient deep feature extraction and classification for identifying defective photovoltaic module cells in electroluminescence images’, expert syst. appl., vol. 175, p. 114810, aug. 2021, doi: 10.1016/j.eswa.2021.114810. [Google Scholar]
- C. Schuss et al., ‘detecting defects in photovoltaic panels with the help of synchronized thermography’, ieee trans. instrum. meas., vol. 67, no. 5, pp. 1178–1186, may 2018, doi: 10.1109/tim.2018.2809078. [Google Scholar]
- M. W. Akram et al., ‘cnn based automatic detection of photovoltaic cell defects in electroluminescence images’, energy, vol. 189, p. 116319, dec. 2019, doi: 10.1016/j.energy.2019.116319. [Google Scholar]
- D.-M. Tsai, S.-C. Wu, and W.-C. Li, ‘defect detection of solar cells in electroluminescence images using fourier image reconstruction’, 9th int. meet. electrochromism, vol. 99, pp. 250–262, apr. 2012, doi: 10.1016/j.solmat.2011.12.007. [Google Scholar]
- H. Munawer Al-Otum, ‘deep learning-based automated defect classification in electroluminescence images of solar panels’, adv. eng. inform., vol. 58, p. 102147, oct. 2023, doi: 10.1016/j.aei.2023.102147. [Google Scholar]
- M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, ‘mobilenetv2: inverted residuals and linear bottlenecks’, presented at the proceedings of the ieee conference on computer vision and pattern recognition, 2018, pp. 4510–4520. [Google Scholar]
- C. Shorten and T. M. Khoshgoftaar, ‘a survey on image data augmentation for deep learning’, j. big data, vol. 6, no. 1, pp. 1–48, 2019. [Google Scholar]
- T. Lai, B. G. Potter, and K. Simmons-Potter, ‘electroluminescence image analysis of a photovoltaic module under accelerated lifecycle testing’, appl. opt., vol. 59, no. 22, pp. g225–g233, aug. 2020, doi: 10.1364/ao.391957. [Google Scholar]
- H. Munawer Al-Otum, ‘classification of anomalies in electroluminescence images of solar pv modules using cnn-based deep learning’, sol. energy, vol. 278, p. 112803, aug. 2024, doi: 10.1016/j.solener.2024.112803. [Google Scholar]
- A. Korovin et al., ‘anomaly detection in electroluminescence images of heterojunction solar cells’, sol. energy, vol. 259, pp. 130–136, july 2023, doi: 10.1016/j.solener.2023.04.059. [Google Scholar]
- C. Buerhop et al., a benchmark for visual identification of defective solar cells in electroluminescence imagery. 2018. doi: 10.4229/35theupvsec20182018-5cv.3.15. [Google Scholar]
- C. Buerhop-Lutz et al., ‘a benchmark for visual identification of defective solar cells in electroluminescence imagery’, presented at the 35th european pv solar energy conference and exhibition, 2018, pp. 1287–1289. [Google Scholar]
- S. Deitsch et al., ‘segmentation of photovoltaic module cells in uncalibrated electroluminescence images’, mach. vis. appl., vol. 32, no. 4, p. 84, 2021. [Google Scholar]
- S. Deitsch et al., ‘automatic classification of defective photovoltaic module cells in electroluminescence images’, sol. energy, vol. 185, pp. 455–468, 2019. [Google Scholar]
- H. A. Al-Otum, ‘automatic defect detection and classification in electroluminescence images of pv cells using convolutional neural networks’, 2023, ssrn. doi: 10.2139/ssrn.4422413. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.