Analysis on main influence factors for silicon rod defects detection model using machine learning

Analysis on main influence factors for silicon rod defects detection model using machine learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis on main influence factors for silicon rod defects detection model using machine learning Chunli Wang, Mingxue Ma, Shuang Zhou, Wenjie Ding, Hongtao Hao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6478319/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The defects detection for silicon rod surface is accessible to data volume, detection model, light conditions and other factors, which affects the accuracy of results. In this paper, a flaw detection test system for silicon surface is built based on the framework of YOLOv5. An actual industrial test and experimental analysis are conducted to study the influence of data volume, light conditions, detection model on the performance of detection system. It is found that the data can be reduced by 27M bytes using YOLOv5s detection model compared with using of YOLOv5m model. The raw defects data can be increased to 3.12 times the original level by data enhancement. The optimal length of strip light source is 800-1000mm, and the lighting uniformity is 0.0231–0.0256. The experimental results show that the inference time for a single image is reduced by 30ms and the recall rate is improved by 0.01 when under YOLOv5s detection model. The defect detection rate is improved by 6.9%, with multi-check rate reduced by 3.2% and missed detection rate redcued by 0.7%, wheh the confidence level is 0.4. The experimental results of influence factors on the defects detection model provide a foundation for the detection during the production of silicon rod. silicon rod defect detection YOLOv5s data enhancement lighting system multiple confidence level Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 May, 2025 Reviewers invited by journal 06 May, 2025 Editor assigned by journal 19 Apr, 2025 Submission checks completed at journal 19 Apr, 2025 First submitted to journal 18 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6478319","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452771827,"identity":"206036ea-cbef-4f00-8c44-864aa92720ac","order_by":0,"name":"Chunli Wang","email":"","orcid":"","institution":"Ningxia University","correspondingAuthor":false,"prefix":"","firstName":"Chunli","middleName":"","lastName":"Wang","suffix":""},{"id":452771828,"identity":"7e4879b5-e15c-4c7f-9f3a-4047b614cd0b","order_by":1,"name":"Mingxue Ma","email":"","orcid":"","institution":"Ningxia 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