Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images
In the pursuit of promoting green energy, efficient defect inspection in solar cell manufacturing is crucial in enhancing the reliability of solar energy systems. However, traditional deep learning models for automatic defect inspection in photovoltaic (PV) cell electroluminescence (EL) images encou...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10979306/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849328599345135616 |
|---|---|
| author | Yuanjun Guan Yang Liu Jiayi Wang Tao Wang Qianchuan Yi Wenxin Jiang Xiaopu Gu Yichen Zhang Li Zhang Tianyan Han Binbing Huang Lilei Hu |
| author_facet | Yuanjun Guan Yang Liu Jiayi Wang Tao Wang Qianchuan Yi Wenxin Jiang Xiaopu Gu Yichen Zhang Li Zhang Tianyan Han Binbing Huang Lilei Hu |
| author_sort | Yuanjun Guan |
| collection | DOAJ |
| description | In the pursuit of promoting green energy, efficient defect inspection in solar cell manufacturing is crucial in enhancing the reliability of solar energy systems. However, traditional deep learning models for automatic defect inspection in photovoltaic (PV) cell electroluminescence (EL) images encounter challenges in industrial settings due to difficulties associated with data acquisition, imbalance, and variability of defects. This paper presents a novel Multi-Scale Attention Generative Adversarial Network (MAGAN), an innovative GAN-based framework specifically designed for data augmentation in the context of solar cell defect detection. When integrated with automated detection techniques, MAGAN markedly improves the accuracy and efficiency of current models. A method for augmenting image datasets of EL was developed to generate a sufficient quantity of images for training machine learning models, addressing sample scarcity and bolstering CNN-based defect classification accuracy. The core of this approach lies in the application of the MCA (Multi-channel Spatial Attention Mechanism) and GLSA (Gate-like Spatial Attention Mechanism) modules, which enhance feature extraction by leveraging channel attention and spatial attention, respectively, thereby reflecting the most recent advancements in attention mechanism technology. The MCA dissects channels into sub-features across various scales, ensuring detailed attention mapping, whereas the GLSA refines spatial cues with a gating mechanism, shedding computational inefficiencies. The effectiveness of this approach is validated by comprehensive experiments against state-of-the-art deep learning models. The experiments demonstrate the exceptional performance of MAGAN, achieving a low FID score of 141.98 and KID score of 0.106 on complex EL images, surpassing previous models and emphasizing data augmentation’s importance in defect detection. With an industry-leading detection accuracy of 87.3%, this study makes a substantial contribution to mitigating data imbalance. This method enhances quality control in solar cell manufacturing. Additionally, it advances defect inspection in the industrial semiconductor sector. |
| format | Article |
| id | doaj-art-6cf97b0faa524af2ae83b7a8ed8a5288 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6cf97b0faa524af2ae83b7a8ed8a52882025-08-20T03:47:33ZengIEEEIEEE Access2169-35362025-01-0113844098442310.1109/ACCESS.2025.356500210979306Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence ImagesYuanjun Guan0https://orcid.org/0009-0000-5901-099XYang Liu1https://orcid.org/0009-0005-6017-7582Jiayi Wang2https://orcid.org/0009-0002-9109-0285Tao Wang3https://orcid.org/0009-0000-6124-2474Qianchuan Yi4https://orcid.org/0009-0009-4803-1512Wenxin Jiang5Xiaopu Gu6Yichen Zhang7Li Zhang8Tianyan Han9Binbing Huang10Lilei Hu11https://orcid.org/0000-0002-8493-2440School of Microelectronics, Shanghai University, Shanghai, ChinaSchool of Microelectronics, Shanghai University, Shanghai, ChinaSchool of Microelectronics, Shanghai University, Shanghai, ChinaSchool of Microelectronics, Shanghai University, Shanghai, ChinaSchool of Microelectronics, Shanghai University, Shanghai, ChinaSchool of Microelectronics, Shanghai University, Shanghai, ChinaSchool of Microelectronics, Shanghai University, Shanghai, ChinaShanghai Industrial μ Technology Research Institute, Shanghai, ChinaShanghai Industrial μ Technology Research Institute, Shanghai, ChinaShanghai Industrial μ Technology Research Institute, Shanghai, ChinaShanghai Industrial μ Technology Research Institute, Shanghai, ChinaSchool of Microelectronics, Shanghai University, Shanghai, ChinaIn the pursuit of promoting green energy, efficient defect inspection in solar cell manufacturing is crucial in enhancing the reliability of solar energy systems. However, traditional deep learning models for automatic defect inspection in photovoltaic (PV) cell electroluminescence (EL) images encounter challenges in industrial settings due to difficulties associated with data acquisition, imbalance, and variability of defects. This paper presents a novel Multi-Scale Attention Generative Adversarial Network (MAGAN), an innovative GAN-based framework specifically designed for data augmentation in the context of solar cell defect detection. When integrated with automated detection techniques, MAGAN markedly improves the accuracy and efficiency of current models. A method for augmenting image datasets of EL was developed to generate a sufficient quantity of images for training machine learning models, addressing sample scarcity and bolstering CNN-based defect classification accuracy. The core of this approach lies in the application of the MCA (Multi-channel Spatial Attention Mechanism) and GLSA (Gate-like Spatial Attention Mechanism) modules, which enhance feature extraction by leveraging channel attention and spatial attention, respectively, thereby reflecting the most recent advancements in attention mechanism technology. The MCA dissects channels into sub-features across various scales, ensuring detailed attention mapping, whereas the GLSA refines spatial cues with a gating mechanism, shedding computational inefficiencies. The effectiveness of this approach is validated by comprehensive experiments against state-of-the-art deep learning models. The experiments demonstrate the exceptional performance of MAGAN, achieving a low FID score of 141.98 and KID score of 0.106 on complex EL images, surpassing previous models and emphasizing data augmentation’s importance in defect detection. With an industry-leading detection accuracy of 87.3%, this study makes a substantial contribution to mitigating data imbalance. This method enhances quality control in solar cell manufacturing. Additionally, it advances defect inspection in the industrial semiconductor sector.https://ieeexplore.ieee.org/document/10979306/Attention mechanismconvolution neural networkdefect inspectionelectroluminescencegenerative adversarial networkphotovoltaic solar cells |
| spellingShingle | Yuanjun Guan Yang Liu Jiayi Wang Tao Wang Qianchuan Yi Wenxin Jiang Xiaopu Gu Yichen Zhang Li Zhang Tianyan Han Binbing Huang Lilei Hu Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images IEEE Access Attention mechanism convolution neural network defect inspection electroluminescence generative adversarial network photovoltaic solar cells |
| title | Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images |
| title_full | Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images |
| title_fullStr | Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images |
| title_full_unstemmed | Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images |
| title_short | Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images |
| title_sort | novel multi scale attention generative adversarial network for photovoltaic solar cell defect inspection using electroluminescence images |
| topic | Attention mechanism convolution neural network defect inspection electroluminescence generative adversarial network photovoltaic solar cells |
| url | https://ieeexplore.ieee.org/document/10979306/ |
| work_keys_str_mv | AT yuanjunguan novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT yangliu novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT jiayiwang novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT taowang novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT qianchuanyi novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT wenxinjiang novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT xiaopugu novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT yichenzhang novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT lizhang novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT tianyanhan novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT binbinghuang novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages AT lileihu novelmultiscaleattentiongenerativeadversarialnetworkforphotovoltaicsolarcelldefectinspectionusingelectroluminescenceimages |