Enhancing photovoltaic panel inspection using RGB image-based detection and image processing techniques
Photovoltaic (PV) panels have become more common in recent years due to their numerous benefits. However, ensuring that PV panels function optimally and reliably is essential for maximizing their efficiency and durability. Identifying and addressing flaws in PV panels is vital to achieving this goal...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
UiTM Cawangan Perlis
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/127419/1/127419.pdf https://doi.org/10.24191/jcrinn.v10i2.528 https://ir.uitm.edu.my/id/eprint/127419/ https://jcrinn.com/index.php/jcrinn |
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| Summary: | Photovoltaic (PV) panels have become more common in recent years due to their numerous benefits. However, ensuring that PV panels function optimally and reliably is essential for maximizing their efficiency and durability. Identifying and addressing flaws in PV panels is vital to achieving this goal. While thermographic imaging is routinely utilized for defect identification, the potential for using RGB images for this purpose is virtually untapped. This paper intends to investigate using RGB images to recognize PV panel defects, proposing a methodology that integrates image processing techniques such as K-means clustering, Canny edge detection, and grayscale conversion. The results show that defects on PV panels may be successfully discovered by applying Kmeans clustering and Canny edge detection to RGB images with an accuracy of 90.66%. This study sheds light on improving defect identification practices in the PV industry. |
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