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HRNet-based automatic identification of photovoltaic module defects using electroluminescence images.

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Title: HRNet-based automatic identification of photovoltaic module defects using electroluminescence images.
Authors: Zhao, Xiaolong1 (AUTHOR), Song, Chonghui1 (AUTHOR) songchonghui@mail.neu.edu.cn, Zhang, Haifeng1 (AUTHOR), Sun, Xianrui1 (AUTHOR), Zhao, Jing2 (AUTHOR)
Source: Energy. Mar2023, Vol. 267, pN.PAG-N.PAG. 1p.
Subjects: Automatic identification, Electroluminescence, Data augmentation, Image analysis, Feature extraction, Building-integrated photovoltaic systems, Intelligent buildings
Abstract: Electroluminescence (EL) images, which have the high spatial resolution, provide the opportunity to detect tiny defects on the surface of photovoltaic (PV) modules. However, manual analysis of EL images is usually an expensive and time-consuming project and requires extensive expertise. Therefore, automatic defect detection is becoming more and more important in the photovoltaic field. This paper proposes an intelligent algorithm for defect detection of photovoltaic modules based the high-resolution network (HRNet). First, aiming at the problem of insufficient data, a data augmentation method is designed to expand the dataset of EL images. Next, an identification algorithm adapted to the image model, called the self-fusion network (SeFNet), is improved. Here, we use the SeFNet to replace the classification layer in the HRNet. SeFNet allows better feature fusion of multi-resolution information in image models. At the same time, it utilizes the improved asymmetric convolution module to enhance the convolution kernel performance through parallel triple operations, so it improves the classification accuracy. Multiple evaluation metrics in the experiment show that the proposed method has better defect recognition performance. • Augmenting datasets and balancing data categories through progressive GAN. • The whole process of feature extraction maintains high resolution. • A novel IAC module is proposed to enhance convolution kernel performance. • A novel SeF module is proposed to enhance channel information fusion. [ABSTRACT FROM AUTHOR]
Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 161740523
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: HRNet-based automatic identification of photovoltaic module defects using electroluminescence images.
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  Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Xiaolong%22">Zhao, Xiaolong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Chonghui%22">Song, Chonghui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> songchonghui@mail.neu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Haifeng%22">Zhang, Haifeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Xianrui%22">Sun, Xianrui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Jing%22">Zhao, Jing</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energy%22">Energy</searchLink>. Mar2023, Vol. 267, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Automatic+identification%22">Automatic identification</searchLink><br /><searchLink fieldCode="DE" term="%22Electroluminescence%22">Electroluminescence</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Building-integrated+photovoltaic+systems%22">Building-integrated photovoltaic systems</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+buildings%22">Intelligent buildings</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Electroluminescence (EL) images, which have the high spatial resolution, provide the opportunity to detect tiny defects on the surface of photovoltaic (PV) modules. However, manual analysis of EL images is usually an expensive and time-consuming project and requires extensive expertise. Therefore, automatic defect detection is becoming more and more important in the photovoltaic field. This paper proposes an intelligent algorithm for defect detection of photovoltaic modules based the high-resolution network (HRNet). First, aiming at the problem of insufficient data, a data augmentation method is designed to expand the dataset of EL images. Next, an identification algorithm adapted to the image model, called the self-fusion network (SeFNet), is improved. Here, we use the SeFNet to replace the classification layer in the HRNet. SeFNet allows better feature fusion of multi-resolution information in image models. At the same time, it utilizes the improved asymmetric convolution module to enhance the convolution kernel performance through parallel triple operations, so it improves the classification accuracy. Multiple evaluation metrics in the experiment show that the proposed method has better defect recognition performance. • Augmenting datasets and balancing data categories through progressive GAN. • The whole process of feature extraction maintains high resolution. • A novel IAC module is proposed to enhance convolution kernel performance. • A novel SeF module is proposed to enhance channel information fusion. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.energy.2022.126605
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      – Code: eng
        Text: English
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      – SubjectFull: Automatic identification
        Type: general
      – SubjectFull: Electroluminescence
        Type: general
      – SubjectFull: Data augmentation
        Type: general
      – SubjectFull: Image analysis
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      – SubjectFull: Feature extraction
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      – SubjectFull: Building-integrated photovoltaic systems
        Type: general
      – SubjectFull: Intelligent buildings
        Type: general
    Titles:
      – TitleFull: HRNet-based automatic identification of photovoltaic module defects using electroluminescence images.
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            NameFull: Zhao, Xiaolong
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            NameFull: Song, Chonghui
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            NameFull: Zhang, Haifeng
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            NameFull: Sun, Xianrui
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            NameFull: Zhao, Jing
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            – D: 15
              M: 03
              Text: Mar2023
              Type: published
              Y: 2023
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              Value: 267
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