Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms
This research study introduces a unique method that makes use of a wide range of deep learning (DL) techniques for automated flaw identification in solar cell images. The research paper investigates how well 24 distinct convolutional neural network (CNN) architectures— Residual network (R...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10820315/ |
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| author | Montaser Abdelsattar Ahmed Abdelmoety Mohamed A. Ismeil Ahmed Emad-Eldeen |
| author_facet | Montaser Abdelsattar Ahmed Abdelmoety Mohamed A. Ismeil Ahmed Emad-Eldeen |
| author_sort | Montaser Abdelsattar |
| collection | DOAJ |
| description | This research study introduces a unique method that makes use of a wide range of deep learning (DL) techniques for automated flaw identification in solar cell images. The research paper investigates how well 24 distinct convolutional neural network (CNN) architectures— Residual network (ResNet), densely connected convolutional networks (DenseNet), visual geometry group (VGG), Inception, mobile network (MobileNet), Xception, SqueezeNet, and AlexNet—classify solar cells into defected and non-defective categories. This study is interesting since it does a thorough assessment of a wide variety of models and concentrates on high-performance architectures and lightweight models that may be used in contexts with limited resources. The research paper performed our studies using a balanced and well-curated dataset of 3,102 images of solar cells with a range of common faults. MobileNetV2 and Xception demonstrated excellent performance in defect identification, with accuracy rates of 99.95% and 99.29% respectively, with minimal validation losses. This study demonstrates the potential of efficient models such as MobileNetV2 for real-world use in solar energy generation. It also provides a detailed comparison of several DL models. The results suggest that the inclusion of these models might significantly enhance quality control systems, offering a reliable and efficient method for detecting flaws in solar cells. |
| format | Article |
| id | doaj-art-c4c91d44f8124d1a84c954e84430717f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c4c91d44f8124d1a84c954e84430717f2025-01-10T00:01:43ZengIEEEIEEE Access2169-35362025-01-01134136415710.1109/ACCESS.2024.352518310820315Automated Defect Detection in Solar Cell Images Using Deep Learning AlgorithmsMontaser Abdelsattar0https://orcid.org/0000-0002-8782-9350Ahmed Abdelmoety1https://orcid.org/0009-0007-3567-6069Mohamed A. Ismeil2https://orcid.org/0000-0002-9885-8501Ahmed Emad-Eldeen3Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena, EgyptDepartment of Electrical Engineering, Faculty of Engineering, South Valley University, Qena, EgyptElectrical Engineering Department, Faculty of Engineering, King Khalid University, Abha, Saudi ArabiaRenewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences “PSAS,” Beni-Suef University, Beni Suef, EgyptThis research study introduces a unique method that makes use of a wide range of deep learning (DL) techniques for automated flaw identification in solar cell images. The research paper investigates how well 24 distinct convolutional neural network (CNN) architectures— Residual network (ResNet), densely connected convolutional networks (DenseNet), visual geometry group (VGG), Inception, mobile network (MobileNet), Xception, SqueezeNet, and AlexNet—classify solar cells into defected and non-defective categories. This study is interesting since it does a thorough assessment of a wide variety of models and concentrates on high-performance architectures and lightweight models that may be used in contexts with limited resources. The research paper performed our studies using a balanced and well-curated dataset of 3,102 images of solar cells with a range of common faults. MobileNetV2 and Xception demonstrated excellent performance in defect identification, with accuracy rates of 99.95% and 99.29% respectively, with minimal validation losses. This study demonstrates the potential of efficient models such as MobileNetV2 for real-world use in solar energy generation. It also provides a detailed comparison of several DL models. The results suggest that the inclusion of these models might significantly enhance quality control systems, offering a reliable and efficient method for detecting flaws in solar cells.https://ieeexplore.ieee.org/document/10820315/Computer visiondefect detectiondeep learningimage classificationphotovoltaics |
| spellingShingle | Montaser Abdelsattar Ahmed Abdelmoety Mohamed A. Ismeil Ahmed Emad-Eldeen Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms IEEE Access Computer vision defect detection deep learning image classification photovoltaics |
| title | Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms |
| title_full | Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms |
| title_fullStr | Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms |
| title_full_unstemmed | Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms |
| title_short | Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms |
| title_sort | automated defect detection in solar cell images using deep learning algorithms |
| topic | Computer vision defect detection deep learning image classification photovoltaics |
| url | https://ieeexplore.ieee.org/document/10820315/ |
| work_keys_str_mv | AT montaserabdelsattar automateddefectdetectioninsolarcellimagesusingdeeplearningalgorithms AT ahmedabdelmoety automateddefectdetectioninsolarcellimagesusingdeeplearningalgorithms AT mohamedaismeil automateddefectdetectioninsolarcellimagesusingdeeplearningalgorithms AT ahmedemadeldeen automateddefectdetectioninsolarcellimagesusingdeeplearningalgorithms |