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Enhancing Surface Defect Detection in Solar Panels with AI-Driven VGG Models

By
Naima El Yanboiy ,
Naima El Yanboiy

Optoelectronics and Applied Energy Techniques, Faculty of science and technology, Moulay Ismail University of Meknes, Errachidia, Morocco

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Mohamed Khala ,
Mohamed Khala

Optoelectronics and Applied Energy Techniques, Faculty of science and technology, Moulay Ismail University of Meknes, Errachidia, Morocco

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Ismail Elabbassi ,
Ismail Elabbassi

Optoelectronics and Applied Energy Techniques, Faculty of science and technology, Moulay Ismail University of Meknes, Errachidia, Morocco

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Nourddine Elhajrat ,
Nourddine Elhajrat

Optoelectronics and Applied Energy Techniques, Faculty of science and technology, Moulay Ismail University of Meknes, Errachidia, Morocco

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Omar Eloutassi ,
Omar Eloutassi

Materials and Modelling Laboratory, Department of Physics, Faculty of Science, Moulay Ismail University of Meknes, Meknes, Morocco

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Youssef El Hassouani ,
Youssef El Hassouani

New Energies and Materials Engineering, Faculty of science and technology, Moulay Ismail University of Meknes, Errachidia, Morocco

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Choukri Messaoudi ,
Choukri Messaoudi

Optoelectronics and Applied Energy Techniques, Faculty of science and technology, Moulay Ismail University of Meknes, Errachidia, Morocco

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Abstract

In recent years, the demand for solar energy has increased considerably. This growing demand has created a corresponding need for solar panel systems that not only demonstrate efficiency, but also guarantee reliability. However, The per-formance and durability of solar panels can be significantly affected by diverse faults such as surface defects, cracks, hot spots and accumulations of dust. Thus, early detection is crucial to ensure optimal operation of solar panels. In this study, we propose an intelligent system for detecting surface defects on solar panels us-ing the Visual Geometry Group (VGG) models. A camera is utilized to capture images of solar panels in both normal and defective states, these images are sub-sequently fed into the trained VGG model, which analyzes and processes them to identify defects on the surface of the solar panel. The experimental results show that the VGG19 model outperforms the VGG16 model in detecting faulty solar panels. VGG19 achieved a precision of 80%, a recall of 1, and an F1 score of 89%, while VGG16 achieved a precision of 79%, a recall of 92%, and an F1 score of 85%. Furthermore, the system demonstrated a high accuracy for the VGG19 in detecting surface panels in their normal state, while for the VGG16 it only achieved 90%. The results demonstrate the ability of the VGG19 model to detect surface defects on solar panels based on visual analysis.

How to Cite

1.
El Yanboiy N, Khala M, Elabbassi I, Elhajrat N, Eloutassi O, El Hassouani Y, Messaoudi C. Enhancing Surface Defect Detection in Solar Panels with AI-Driven VGG Models. Data and Metadata [Internet]. 2023 Dec. 28 [cited 2024 Apr. 22];2:81. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/81

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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