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Integrating deep transfer learning and image enhancement for enhancing defective photovoltaic cells classification in electroluminescence images

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cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department da75dbd1-11a6-49dd-ae82-ac7efb3405da
cris.virtualsource.department aae06383-c94d-4c7b-aa89-80ee384b5800
cris.virtualsource.department 0ba13a36-f128-4773-a456-a52306b8c31c
dc.contributor.author Aimi Salihah Abdul Nasir
dc.contributor.author Mohammad Faridun Naim Tajuddin
dc.contributor.author Kumuthawathe Ananda-Rao
dc.contributor.author Hanim Suraya Mohd Mokhtar
dc.contributor.author Muhammad Hafeez Abdul Nasir
dc.date.accessioned 2026-02-02T06:58:13Z
dc.date.available 2026-02-02T06:58:13Z
dc.date.issued 2025-06
dc.description.abstract 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.
dc.identifier.doi 10.22068/IJEEE.21.2.3571
dc.identifier.uri https://ijeee.iust.ac.ir/browse.php?a_id=3571&slc_lang=en&sid=1&printcase=1&hbnr=1&hmb=1
dc.identifier.uri https://hdl.handle.net/20.500.14170/15968
dc.language.iso en
dc.publisher Iran University of Science and Technology
dc.relation.ispartof Iranian Journal of Electrical and Electronic Engineering
dc.relation.ispartofseries Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia
dc.relation.issn 1735-2827
dc.subject Defect Classification
dc.subject Electroluminescence
dc.subject Multi-Scale Retinex (MSR)
dc.subject Multi-Scale Retinex 2 (MSR2)
dc.subject Photovoltaic (PV)
dc.subject Pre-Trained Models
dc.title Integrating deep transfer learning and image enhancement for enhancing defective photovoltaic cells classification in electroluminescence images
dc.type Resource Types::text::journal::journal article
dspace.entity.type Publication
oaire.citation.endPage 15
oaire.citation.issue 2
oaire.citation.startPage 1
oaire.citation.volume 21
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Sains Malaysia
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