Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)                   IJEEE 2025, 21(2): 3571-3571 | Back to browse issues page

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Mohd Mokhtar H S, Abdul Nasir A S, Naim Tajuddin M F, Abdul Nasir M H, Ananda Rao K. Integrating Deep Transfer Learning and Image Enhancement for Enhancing Defective Photovoltaic Cells Classification in Electroluminescence Images. IJEEE 2025; 21 (2) :3571-3571
URL: http://ijeee.iust.ac.ir/article-1-3571-en.html
Integrating Deep Transfer Learning and Image Enhancement for Enhancing Defective Photovoltaic Cells Classification in Electroluminescence Images
Hanim Suraya Mohd Mokhtar , Aimi Salihah Abdul Nasir , Mohammad Faridun Naim Tajuddin , Muhammad Hafeez Abdul Nasir , Kumuthawathe Ananda Rao
Abstract:   (223 Views)
The rapid growth of photovoltaic (PV) systems has highlighted the need for efficient and reliable defect detection to maintain system performance. Electroluminescence (EL) imaging has emerged as a promising technique for identifying defects in PV cells; however, challenges remain in accurately classifying defects due to the variability in image quality and the complex nature of the defects. Existing studies often focus on single image enhancement techniques or fail to comprehensively compare the performance of various image enhancement methods across different deep learning (DL) models. This research addresses these gaps by proposing an in-depth analysis of the impact of multiple image enhancement techniques on defect detection performance, using various deep learning models of low, medium, and high complexity. The results demonstrate that mid-complexity models, especially DarkNet-53, achieve the highest performance with an accuracy of 94.55% after MSR2 enhancement. DarkNet-53 consistently outperformed both lower-complexity models and higher-complexity models in terms of accuracy, precision, and F1-score. The findings highlight that medium-depth models, enhanced with MSR2, offer the most reliable results for photovoltaic defect detection, demonstrating a significant improvement over other models in terms of accuracy and efficiency. This research provides valuable insights for optimizing defect detection systems in photovoltaic applications, emphasizing the importance of both model complexity and image enhancement techniques for robust performance.
Keywords: Photovoltaic (PV), Defect Classification, Electroluminescence, Multi-Scale Retinex (MSR), Multi-Scale Retinex 2 (MSR2), Pre-Trained Models.
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Type of Study: Only For Articles of ELECRiS 2024 | Subject: Image Processing
Received: 2024/12/01 | Revised: 2025/03/13 | Accepted: 2025/02/18
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.