Mejora de la detección de defectos superficiales en paneles solares con modelos VGG basados en IA Data and Metadata. 2023; 2:81 doi: 10.56294/dm202381 ORIGINAL Enhancing Surface Defect Detection in Solar Panels with AI-Driven VGG Models Naima El Yanboiy 1 , Mohamed Khala 1 , Ismail Elabbassi 1 , Omar Eloutassi 2 , Youssef El Hassouani 3 , Choukri Messaoudi 1 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 performance 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 using 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 subsequently 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. Keywords: Smart Detection; Surface Defects; Anomaly Detection; Solar Panel; VGG Model. RESUMEN En los últimos años, la demanda de energía solar ha aumentado considerablemente. Esta creciente demanda ha creado la correspondiente necesidad de sistemas de paneles solares que no sólo demuestren eficiencia, sino que también garanticen fiabilidad. Sin embargo, el rendimiento y la durabilidad de los paneles solares pueden verse considerablemente afectados por diversos fallos, como defectos superficiales, grietas, puntos calientes y acumulaciones de polvo. Por ello, la detección precoz es crucial para garantizar el funcionamiento óptimo de los paneles solares. En este estudio, proponemos un sistema inteligente para detectar defectos superficiales en paneles solares utilizando los modelos de Visual Geometry Group (VGG). Se utiliza una cámara para capturar imágenes de paneles solares tanto en estado normal como defectuoso, estas imágenes se introducen posteriormente en el modelo VGG entrenado, que las analiza y procesa para identificar defectos en la superficie del panel solar. © 2023; Los autores. Este es un artículo en acceso abierto, distribuido bajo los términos de una licencia Creative Commons (https:// creativecommons.org/licenses/by/4.0) que permite el uso, distribución y reproducción en cualquier medio siempre que la obra original sea correctamente citada 1 Optoelectronics and Applied Energy Techniques, Faculty of science and technology, Moulay Ismail University of Meknes. Errachidia, Morocco. 2 Materials and Modelling Laboratory, Department of Physics, Faculty of Science, Moulay Ismail University of Meknes. Meknes, Morocco. 3 New Energies and Materials Engineering, Faculty of science and technology, Moulay Ismail University of Meknes. Errachidia, Morocco. Cite as: Yanboiy NE, Khala M, Elabbassi I, Elhajrat N, Eloutassi O, Hassouani YE, et al. Mejora de la detección de defectos superficiales en paneles solares con modelos VGG basados en IA. Data and Metadata 2023; 2:81. https://doi.org/10.56294/dm202381 Submitted: 02-09-2023 Revised: 12-11-2023 Accepted: 27-12-2023 Published: 28-12-2023 Editor: Javier González Argote Guest Editor: Yousef Farhaoui Note: paper presented at the International Conference on Artificial Intelligence and Smart Environments (ICAISE’2023).