Extraction of Solar Panel Image Texture Feature Using GLCM Method for Damage Analysis on Solar Panel Surface Images
Existing techniques for assessing solar panel surface damage frequently lack precision in differentiating defect kinds, necessitating a dependable automated solution. Defects like cracks and scratches substantially diminish panel efficiency, underscoring the necessity of robust analytical procedures...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/22/e3sconf_interconnects2025_01001.pdf |
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| author | Wiliani Ninuk Khawa Titik Ramli Suzaimah |
| author_facet | Wiliani Ninuk Khawa Titik Ramli Suzaimah |
| author_sort | Wiliani Ninuk |
| collection | DOAJ |
| description | Existing techniques for assessing solar panel surface damage frequently lack precision in differentiating defect kinds, necessitating a dependable automated solution. Defects like cracks and scratches substantially diminish panel efficiency, underscoring the necessity of robust analytical procedures. This study seeks to validate the Gray Level Cooccurrence Matrix (GLCM) technique for extracting texture information to identify and analyze damage on solar panel surfaces. This method utilizes Python software and a dataset of solar panel surface photos to accurately distinguish between damaged and undamaged surfaces. The spot category demonstrates the lowest homogeneity (5636.922) and contrast (5632.922), signifying a smoother yet less uniform texture. Energy values are predominantly low across all categories, with marginally higher consistency in fractures (0.005) relative to others (0.002). The results indicate that faults enhance unpredictability and randomization in texture relative to the homogeneity of intact surfaces. These insights facilitate precise damage identification and enhanced maintenance plans. This research provides advancements in renewable energy, materials science, and computer vision, applicable to solar panel maintenance, quality assurance, and automated flaw identification within the photovoltaic sector. |
| format | Article |
| id | doaj-art-e64c998c937746dfaa7a47e66fe60cc8 |
| institution | OA Journals |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-e64c998c937746dfaa7a47e66fe60cc82025-08-20T02:09:34ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016220100110.1051/e3sconf/202562201001e3sconf_interconnects2025_01001Extraction of Solar Panel Image Texture Feature Using GLCM Method for Damage Analysis on Solar Panel Surface ImagesWiliani Ninuk0Khawa Titik1Ramli Suzaimah2Department of Informatics Engineering, Faculty of Engineering, University of PancasilaDepartment of Computer Science, Asia e UniversityDepartement of Computer Science, National Defence University of MalaysiaExisting techniques for assessing solar panel surface damage frequently lack precision in differentiating defect kinds, necessitating a dependable automated solution. Defects like cracks and scratches substantially diminish panel efficiency, underscoring the necessity of robust analytical procedures. This study seeks to validate the Gray Level Cooccurrence Matrix (GLCM) technique for extracting texture information to identify and analyze damage on solar panel surfaces. This method utilizes Python software and a dataset of solar panel surface photos to accurately distinguish between damaged and undamaged surfaces. The spot category demonstrates the lowest homogeneity (5636.922) and contrast (5632.922), signifying a smoother yet less uniform texture. Energy values are predominantly low across all categories, with marginally higher consistency in fractures (0.005) relative to others (0.002). The results indicate that faults enhance unpredictability and randomization in texture relative to the homogeneity of intact surfaces. These insights facilitate precise damage identification and enhanced maintenance plans. This research provides advancements in renewable energy, materials science, and computer vision, applicable to solar panel maintenance, quality assurance, and automated flaw identification within the photovoltaic sector.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/22/e3sconf_interconnects2025_01001.pdf |
| spellingShingle | Wiliani Ninuk Khawa Titik Ramli Suzaimah Extraction of Solar Panel Image Texture Feature Using GLCM Method for Damage Analysis on Solar Panel Surface Images E3S Web of Conferences |
| title | Extraction of Solar Panel Image Texture Feature Using GLCM Method for Damage Analysis on Solar Panel Surface Images |
| title_full | Extraction of Solar Panel Image Texture Feature Using GLCM Method for Damage Analysis on Solar Panel Surface Images |
| title_fullStr | Extraction of Solar Panel Image Texture Feature Using GLCM Method for Damage Analysis on Solar Panel Surface Images |
| title_full_unstemmed | Extraction of Solar Panel Image Texture Feature Using GLCM Method for Damage Analysis on Solar Panel Surface Images |
| title_short | Extraction of Solar Panel Image Texture Feature Using GLCM Method for Damage Analysis on Solar Panel Surface Images |
| title_sort | extraction of solar panel image texture feature using glcm method for damage analysis on solar panel surface images |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/22/e3sconf_interconnects2025_01001.pdf |
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