Enhancing Solar Panel Fault Detection Using VGG16 with Data Augmentation and Transfer Learning
DOI:
https://doi.org/10.61704/pr.710Keywords:
Solar Panels, CNN, VGG16, Image Classification, Data AugmentationAbstract
Surface defects in solar panels have a detrimental impact on the performance and reliability of the system. In this research paper, we proposed an automatic fault detection model for solar panels using a deep learning model based on the VGG16 (CNN) architecture with transfer learning and data augmentation. A dataset that has a total of 869 images for six fault categories (Clean, Dusty, Bird-drop, Electrical-damage, Physical-damage, Snow) was used. Two models were utilized for analysis: (Model 1) the baseline VGG16 which is the VGG16 architecture pre-trained on the original dataset scope; and (Model 2) the enhanced VGG16 which incorporates data augmentation, freezing of early layers and dropout regularizing. Model 2 achieved a higher accuracy at 98.76% on validation set compared to Model 1 with an accuracy of 82.49% indicating that comprising transfer learning along and pertinent augmentation positively enhances the classification accuracy. The proposed approach is capable of being scaled up and replicated in real-world applications of smart automated solar panel inspection.
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Copyright (c) 2026 Yahya A. Yahya
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Copyright © 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). You may not alter or transform this work in any way without permission from the authors. Non-commercial use, distribution, and copying are permitted, provided that appropriate credit is given to the authors and Al-Hadba University.
