{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/solar-cell-surface-defect-inspection-based-on","title":"Solar Cell Surface Defect Inspection Based on Multispectral Convolutional Neural Network","arxiv_id":"1812.06220","date":"2018-12-15","proceeding":null,"authors":["Haiyong Chen","Yue Pang","Qidi Hu","Kun Liu"],"abstract":"Similar and indeterminate defect detection of solar cell surface with\nheterogeneous texture and complex background is a challenge of solar cell\nmanufacturing. The traditional manufacturing process relies on human eye\ndetection which requires a large number of workers without a stable and good\ndetection effect. In order to solve the problem, a visual defect detection\nmethod based on multi-spectral deep convolutional neural network (CNN) is\ndesigned in this paper. Firstly, a selected CNN model is established. By\nadjusting the depth and width of the model, the influence of model depth and\nkernel size on the recognition result is evaluated. The optimal CNN model\nstructure is selected. Secondly, the light spectrum features of solar cell\ncolor image are analyzed. It is found that a variety of defects exhibited\ndifferent distinguishable characteristics in different spectral bands. Thus, a\nmulti-spectral CNN model is constructed to enhance the discrimination ability\nof the model to distinguish between complex texture background features and\ndefect features. Finally, some experimental results and K-fold cross validation\nshow that the multi-spectral deep CNN model can effectively detect the solar\ncell surface defects with higher accuracy and greater adaptability. The\naccuracy of defect recognition reaches 94.30%. Applying such an algorithm can\nincrease the efficiency of solar cell manufacturing and make the manufacturing\nprocess smarter.","url_abs":"http://arxiv.org/abs/1812.06220v1","url_pdf":"http://arxiv.org/pdf/1812.06220v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"defect-detection","task_name":"Defect Detection"},{"task_slug":"multi-document-summarization","task_name":"Multi-Document Summarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-document-summarization-on-review","task":"Multi-Document Summarization","dataset":"review","model":"solar","rank_in_archive_order":1,"of":1,"metrics":{"1-of-100 Accuracy":"100"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}