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Research on multi-defects classification detection method for solar cells based on deep learning.

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Title: Research on multi-defects classification detection method for solar cells based on deep learning.
Authors: Li, Zhenwei, Zhang, Shihai, Qu, Chongnian, Zhang, Zimiao, Sun, Feng
Source: PLoS ONE; 6/21/2024, Vol. 19 Issue 6, p1-16, 16p
Subject Terms: DEEP learning, SOLAR cells, SOLAR cell manufacturing, MAXIMUM power point trackers, K-means clustering, MANUFACTURING defects, CLASSIFICATION
Abstract: Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface defects are divided into three categories, which include difficult-detecting defects (mismatch), general defects (bubble, glass-crack and cell-crack) and easy-detecting defects (glass-upside-down). Corresponding to different types of defects, the deep learning model with different optimization methods and a classification detection method based on multi-models fusion are proposed in the paper. In the proposed model, in order to solve the mismatch problem between the default anchor boxes size of YOLOv5s model and the extreme scale of the battery mismatch defect label boxes, the K-means algorithm was adopted to re-cluster the dedicated anchor boxes for the mismatch defect label boxes. In order to improve the comprehensive detection accuracy of YOLOv5s model for the general defects, the YOLOv5s model was also improved by the methods of image preprocessing, anchor box improving and detection head replacing. In order to ensure the recognition accuracy and improve the detection speed for easy-detecting defects, the lightweight classification network MobileNetV2 was also used to classify the cells with glass-upside-down defects. The experimental results show that the proposed optimization model and classification detection method can significantly improve the defect detection precision. Respectively, the detection precision for mismatch, bubble, glass-crack and cell-crack defects are up to 95.64%, 91.8%, 93.1% and 98.0%. By using lightweight model to train the glass-upside-down defect dataset, the average classification accuracy reaches 100% and the detection speed reaches 13.29 frames per second. The comparison experiments show that the proposed model has a great improvement in detection accuracy compared with the original model, and the defect detection speed of lightweight classification network is improved more obviously, which confirms the effectiveness of the proposed optimization model and the multi-defect classification detection method for solar cells defect detection. [ABSTRACT FROM AUTHOR]
Copyright of PLoS ONE is the property of Public Library of 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.)
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  Data: Research on multi-defects classification detection method for solar cells based on deep learning.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Zhenwei%22">Li, Zhenwei</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shihai%22">Zhang, Shihai</searchLink><br /><searchLink fieldCode="AR" term="%22Qu%2C+Chongnian%22">Qu, Chongnian</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zimiao%22">Zhang, Zimiao</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Feng%22">Sun, Feng</searchLink>
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  Data: PLoS ONE; 6/21/2024, Vol. 19 Issue 6, p1-16, 16p
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  Data: <searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22SOLAR+cells%22">SOLAR cells</searchLink><br /><searchLink fieldCode="DE" term="%22SOLAR+cell+manufacturing%22">SOLAR cell manufacturing</searchLink><br /><searchLink fieldCode="DE" term="%22MAXIMUM+power+point+trackers%22">MAXIMUM power point trackers</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22MANUFACTURING+defects%22">MANUFACTURING defects</searchLink><br /><searchLink fieldCode="DE" term="%22CLASSIFICATION%22">CLASSIFICATION</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface defects are divided into three categories, which include difficult-detecting defects (mismatch), general defects (bubble, glass-crack and cell-crack) and easy-detecting defects (glass-upside-down). Corresponding to different types of defects, the deep learning model with different optimization methods and a classification detection method based on multi-models fusion are proposed in the paper. In the proposed model, in order to solve the mismatch problem between the default anchor boxes size of YOLOv5s model and the extreme scale of the battery mismatch defect label boxes, the K-means algorithm was adopted to re-cluster the dedicated anchor boxes for the mismatch defect label boxes. In order to improve the comprehensive detection accuracy of YOLOv5s model for the general defects, the YOLOv5s model was also improved by the methods of image preprocessing, anchor box improving and detection head replacing. In order to ensure the recognition accuracy and improve the detection speed for easy-detecting defects, the lightweight classification network MobileNetV2 was also used to classify the cells with glass-upside-down defects. The experimental results show that the proposed optimization model and classification detection method can significantly improve the defect detection precision. Respectively, the detection precision for mismatch, bubble, glass-crack and cell-crack defects are up to 95.64%, 91.8%, 93.1% and 98.0%. By using lightweight model to train the glass-upside-down defect dataset, the average classification accuracy reaches 100% and the detection speed reaches 13.29 frames per second. The comparison experiments show that the proposed model has a great improvement in detection accuracy compared with the original model, and the defect detection speed of lightweight classification network is improved more obviously, which confirms the effectiveness of the proposed optimization model and the multi-defect classification detection method for solar cells defect detection. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of PLoS ONE is the property of Public Library of 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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        Value: 10.1371/journal.pone.0304819
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        Text: English
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      – SubjectFull: SOLAR cell manufacturing
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      – SubjectFull: CLASSIFICATION
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              M: 06
              Text: 6/21/2024
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