Performance Analysis of Classification and Detection for PV Panel Motion Blur Images Based on Deblurring and Deep Learning Techniques

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dc.authoridSyafrudin, Muhammad/0000-0002-5640-4413
dc.authoridFitriyani, Norma Latif/0000-0002-1133-3965
dc.authoridGarcia Marquez, Fausto Pedro/0000-0002-9245-440X
dc.authoridGUNESER, Muhammet Tahir/0000-0003-3502-2034
dc.contributor.authorAl-Dulaimi, Abdullah Ahmed
dc.contributor.authorGuneser, Muhammet Tahir
dc.contributor.authorHameed, Alaa Ali
dc.contributor.authorMarquez, Fausto Pedro Garcia
dc.contributor.authorFitriyani, Norma Latif
dc.contributor.authorSyafrudin, Muhammad
dc.date.accessioned2024-09-29T16:08:15Z
dc.date.available2024-09-29T16:08:15Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractDetecting snow-covered solar panels is crucial as it allows us to remove snow using heating techniques more efficiently and restores the photovoltaic system to proper operation. This paper presents classification and detection performance analyses for snow-covered solar panel images. The classification analysis consists of two cases, and the detection analysis consists of one case based on three backbones. In this study, five deep learning models, namely visual geometry group-16 (VGG-16), VGG-19, residual neural network-18 (RESNET-18), RESNET-50, and RESNET-101, are used to classify solar panel images. The models are trained, validated, and tested under different conditions. The first case of classification is performed on the original dataset without preprocessing. In the second case, extreme climate conditions are simulated by generating motion noise; furthermore, the dataset is replicated using the upsampling technique to handle the unbalancing issue. For the detection case, a region-based convolutional neural network (RCNN) detector is used to detect the three categories of solar panels, which are all_snow, no_snow, and partial. The dataset of these categories is taken from the second case in the classification approach. Finally, we proposed a blind image deblurring algorithm (BIDA) that can be a preprocessing step before the CNN (BIDA-CNN) model. The accuracy of the models was compared and verified; the accuracy results show that the proposed CNN-based blind image deblurring algorithm (BIDA-CNN) outperformed other models evaluated in this study.en_US
dc.identifier.doi10.3390/su15021150
dc.identifier.issn2071-1050
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85158895424en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/su15021150
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7430
dc.identifier.volume15en_US
dc.identifier.wosWOS:000927525200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdeep learningen_US
dc.subjectCNNen_US
dc.subjectimage classificationen_US
dc.subjectsolar panelsen_US
dc.subjectphotovoltaic (PV)en_US
dc.subjectPV image detectionen_US
dc.titlePerformance Analysis of Classification and Detection for PV Panel Motion Blur Images Based on Deblurring and Deep Learning Techniquesen_US
dc.typeArticleen_US

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