Research on multi-defects classification detection method for solar cells based on deep learning
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, g...
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| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0304819 |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0304819</identifier><identifier>PMID: 38905246</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Biology and Life Sciences ; Boxes ; Cameras ; Classification ; Computer and Information Sciences ; Datasets ; Deep Learning ; Defective products ; Defects ; Efficiency ; Engineering and Technology ; Frames per second ; Gallium arsenide ; Image Processing, Computer-Assisted - methods ; Labels ; Lightweight ; Machine learning ; Manufacturing industry ; Methods ; Object recognition ; Optimization ; Optimization models ; Photovoltaic cells ; Physical Sciences ; Research and Analysis Methods ; Silicon ; Solar batteries ; Solar cells ; Solar Energy ; Surface defects ; Weight reduction</subject><ispartof>PloS one, 2024-06, Vol.19 (6), p.e0304819</ispartof><rights>Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 Li et al 2024 Li et al</rights><rights>2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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deep learning</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2024-06-21</date><risdate>2024</risdate><volume>19</volume><issue>6</issue><spage>e0304819</spage><pages>e0304819-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><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><cop>United States</cop><pub>Public Library of Science</pub><pmid>38905246</pmid><doi>10.1371/journal.pone.0304819</doi><tpages>e0304819</tpages><orcidid>https://orcid.org/0000-0002-7725-4493</orcidid><orcidid>https://orcid.org/0009-0006-2466-5420</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Algorithms Biology and Life Sciences Boxes Cameras Classification Computer and Information Sciences Datasets Deep Learning Defective products Defects Efficiency Engineering and Technology Frames per second Gallium arsenide Image Processing, Computer-Assisted - methods Labels Lightweight Machine learning Manufacturing industry Methods Object recognition Optimization Optimization models Photovoltaic cells Physical Sciences Research and Analysis Methods Silicon Solar batteries Solar cells Solar Energy Surface defects Weight reduction |
| title | Research on multi-defects classification detection method for solar cells based on deep learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T20%3A14%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Research%20on%20multi-defects%20classification%20detection%20method%20for%20solar%20cells%20based%20on%20deep%20learning&rft.jtitle=PloS%20one&rft.au=Li,%20Zhenwei&rft.date=2024-06-21&rft.volume=19&rft.issue=6&rft.spage=e0304819&rft.pages=e0304819-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0304819&rft_dat=%3Cgale_plos_%3EA798527647%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3071023243&rft_id=info:pmid/38905246&rft_galeid=A798527647&rft_doaj_id=oai_doaj_org_article_8e547ee5e31343c593322f9b3c3e9ff6&rfr_iscdi=true |