Open Access
| Issue |
MATEC Web Conf.
Volume 417, 2025
2025 RAPDASA-RobMech-PRASA-AMI Conference: Bridging the Gap between Industry & Academia - The 26th Annual International RAPDASA Conference, joined by RobMech, PRASA and AMI, co-hosted by CSIR and Tshwane University of Technology, Pretoria
|
|
|---|---|---|
| Article Number | 04013 | |
| Number of page(s) | 14 | |
| Section | Robotics and Mechatronics | |
| DOI | https://doi.org/10.1051/matecconf/202541704013 | |
| Published online | 25 November 2025 | |
- S. Jaybhaye, O. Thakur, R. Yardi, V. Raut, A. Raut, Solar panel damage detection and localization of thermal images. J. Fail. Anal. Prev. 23, 1980–1990 (2023) [Google Scholar]
- J.K. Kaldellis, A. Kokala, Quantifying the decrease of the photovoltaic panels’ energy yield due to phenomena of natural air pollution disposal. Energy 35, 4862–4869 (2010) [Google Scholar]
- M. Meribout, V.K. Tiwari, J.P. Peña Herrera, A.N.M.A. Baobaid, Solar panel inspection techniques and prospects. Measurement 112466 (2023). https://doi.org/10.1016/j.measurement.2023.112466 [Google Scholar]
- L. Kruitwagen, K.T. Story, J. Friedrich, L. Byers, S. Skillman, C. Hepburn, A global inventory of photovoltaic solar energy generating units. Nature 508, 604–610 (2021) [Google Scholar]
- Y. Higuchi, T. Babasaki, Classification of causes of broken solar panels in solar power plants. in Proc. IEEE INTELEC, 127–132 (2017) [Google Scholar]
- N.Venkatesh, V. Sugumaran, Fault diagnosis of visual faults in photovoltaic modules: A review. J. Energy Eng. (2021). https://doi.org/10.1080/15435075.2020.1825443 [Google Scholar]
- S.N. Venkatesh, V. Sugumaran, Machine vision-based fault diagnosis of photovoltaic modules using a lazy learning approach. Measurement 101 (2022). https://doi.org/10.1016/j.measurement.2022.110786 [Google Scholar]
- S. Kole, C. Agarwal, T. Gupta, S. Singh, SURF and RANSAC: A conglomerative approach to object recognition. Int. J. Comput. Appl. 1°0, 7–9 (2015) https://doi.org/10.5120/19174-0645 [Google Scholar]
- A. Ghahremani, S.D. Adams, M. Norton, S.Y. Khoo, A.Z. Kouzani, Detecting defects in solar panels using the YOLO v10 and v11 algorithms. Electronics 14, 2 (2025). [Google Scholar]
- A.B. Di Renzo, C.R. Zamarreno, C. Martelli, J.C.C. Da Silva, Edge device for ultraviolet fluorescence inspection of photovoltaic panels. in Proc. IEEE Sensors (2023) [Google Scholar]
- W. Zhang, et al., SoilingEdge: PV soiling power loss estimation at the edge using surveillance cameras. IEEE Trans. Sustain. Energy 15, 556–566 (2024). https://doi.org/10.1109/tste.2023.3320690 [Google Scholar]
- Y. Yang, J. Zhang, X. Shu, L. Pan, M. Zhang, A lightweight Transformer model for defect detection in electroluminescence images of photovoltaic cells. IEEE Access (2024). https://doi.org/10.1109/access.2024.3520239 [Google Scholar]
- P. Musa, F. Al Rafi, M. Lamsani, A review: Contrast-limited adaptive histogram equalization (CLAHE) methods to help the application of face recognition. in Proc. 3rd Int. Conf. Informatics and Computing (ICIC) (2018) [Google Scholar]
- Yolo training data augmentation techniques | Restackio, Accessed: May 01, 2025. [Online]. Available: https://www.restack.io/p/data-augmentation-answer-yolo-training-techniques-cat-ai [Google Scholar]
- J.L. Mira, J. Barba, F.P. Romero, M.S. Escolar, J. Caba, J.C. López, Benchmarking of computer vision methods for energy-efficient high-accuracy olive fly detection on edge devices. Multimed. Tools Appl. (2024). https://doi.org/10.1007/s11042-024- 18589-y [Google Scholar]
- T-s. Wang, G.T. Kim, M. Kim, J. Jang, Contrast Enhancement-Based Preprocessing Process to Improve Deep Learning Object Task Performance and Results. Applied Sciences, 13(19), 10760. (2023) https://doi.org/10.3390/app131910760 [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.

