Comparison of Preprocessing Methods Impact on Detection of Soldering Splashes Using Different YOLOv8 Versions

Comparison of Preprocessing Methods Impact on Detection of Soldering Splashes Using Different YOLOv8 Versions | 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 Article Comparison of Preprocessing Methods Impact on Detection of Soldering Splashes Using Different YOLOv8 Versions Peter Klčo, Dušan Koniar, Libor Hargaš, Marek Paškala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4712060/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Quality inspection of electronic boards during manufacturing process is crucial step especially in the case of specific and expensive power electronics modules. Soldering splashes occurrence decreases the reliability and electric properties of final products. The aim of this paper is to compare different YOLOv8 models (small, medium, large) with the combination of basic image preprocessing techniques to achieve the best possible performance of designed algorithm. As preprocessing methods contrast limited adaptive histogram equalization (CLAHE) and image color channels manipulation are used. Results show that suitable combination of YOLOv8 model and preprocessing methods leads to increase the recall parameter. In our inspection task, the recall can be considered as the most important metric. The results are supported by the standard two-way ANOVA test. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Computational science Convolutional neural network YOLOv8 image preprocessing soldering splashes quality inspection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4712060","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":333491023,"identity":"4f954902-6a66-4ee9-b796-1853ad81b36e","order_by":0,"name":"Peter Klčo","email":"","orcid":"","institution":"University of Zilina","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Klčo","suffix":""},{"id":333491024,"identity":"7e977c73-0e5f-449a-bfaf-ae5717ac6e3b","order_by":1,"name":"Dušan Koniar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie2RMUvDQBTH/+WgWR51TagkX+HgIFUM7Vd5EkiWCEKhFBwUOrgEXAt+CUFwDmTIUnENuFQKTg5CloAFjSZCh0Q6OtxvuHcP7sf9Hw/QaP4jyc/JgKjKuwf67dFc/1Z6ywBE+ysVgoLdhx3KIA9finME9sgQr4XH3uEE5K7X2zGcOGlVrDxSwyUidbzoq9uIgyoYjSSTD/l41arIPMKQMD+9S6HEWZl+K67JpoA8aA8m83DzUStGIY74s1HkJZybLoXd6peoUkgJcNIonAIdwazVm3tCMlAypWkvZp8o7c9MTjKSq/bxB1m4eaa5b8un7B4ljyfG9eLBKrcXthNze7I63m4j6tK1SI1Go9HswRfAME8cmx0KtwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Zilina","correspondingAuthor":true,"prefix":"","firstName":"Dušan","middleName":"","lastName":"Koniar","suffix":""},{"id":333491026,"identity":"19cf772f-1528-4163-8b8c-71a2f9e846e2","order_by":2,"name":"Libor Hargaš","email":"","orcid":"","institution":"University of Zilina","correspondingAuthor":false,"prefix":"","firstName":"Libor","middleName":"","lastName":"Hargaš","suffix":""},{"id":333491027,"identity":"62c0b38f-64a3-4537-8598-f7e9f991434f","order_by":3,"name":"Marek Paškala","email":"","orcid":"","institution":"University of Zilina","correspondingAuthor":false,"prefix":"","firstName":"Marek","middleName":"","lastName":"Paškala","suffix":""}],"badges":[],"createdAt":"2024-07-09 12:29:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4712060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4712060/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62092095,"identity":"5f61a18f-ff4d-4faf-9c61-5b736e754f47","added_by":"auto","created_at":"2024-08-09 07:52:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1311820,"visible":true,"origin":"","legend":"","description":"","filename":"ComparisonofPreprocessingMethodsImpact.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4712060/v1_covered_02129d53-586a-44e8-a0b8-9f3a5e21d30a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of Preprocessing Methods Impact on Detection of Soldering Splashes Using Different YOLOv8 Versions","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Convolutional neural network, YOLOv8, image preprocessing, soldering splashes, quality inspection","lastPublishedDoi":"10.21203/rs.3.rs-4712060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4712060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Quality inspection of electronic boards during manufacturing process is crucial step especially in the case of specific and expensive power electronics modules. 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