Target detection of diamond nanostructures based on improved YOLOv8 modeling

Target detection of diamond nanostructures based on improved YOLOv8 modeling | 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 Target detection of diamond nanostructures based on improved YOLOv8 modeling Fengxiang Guo, Xinyun Guo, Lei Guo, Qinhang Wang, Shousheng Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3963300/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 Boron-doped diamond thin films exhibit extensive applications in chemical sensing, in which functionalized nanostructures on the surfaces enhances further the performance of these films. However, targets detecting within such nanostructures faces great challenges such as noise, unclear object boundaries, and mutual occlusion, leading to inaccuracies in existing detection models. To tackle these challenges, we optimized the YOLOv8 model and introduced DWS-YOLOv8 for target detection of diamond nanostructures. The integration of the Deformable Convolutional C2f (DCN_C2f) module into the backbone network allowed adaptive adjustment of the network's receptive field. Moreover, incorporating the Shuffle Attention (SA) mechanism effectively addressed detail loss during convolutional iterations and reduced noise's impact on prediction results. Finally, leveraging Wise-IoU (WIoU) v3 as the bounding box regression loss enhanced the model's focus on diamond nanostructure samples, thereby improving localization capability. Experimental results showcase that compared to YOLOv8, our model achieves a 9.4% higher detection accuracy with reduced computational complexity. Furthermore, the recall rate (R) saw an increase of 0.6%, [email protected] improved by 2.6%, and [email protected] :0.95 increased by 0.6%. Additionally, DWS-YOLOv8 demonstrated enhancements in precision (P), recall (R), [email protected] , and [email protected] :0.95, validating the effectiveness of our approach in enhancing target detection performance. Biological sciences/Computational biology and bioinformatics/Image processing Biological sciences/Computational biology and bioinformatics/Machine learning YOLOv8 DCN_C2f Shuffle Attention WIoU Diamond nanostructure 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-3963300","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":276363145,"identity":"e6cc4fb3-17fa-480e-b4eb-544c5063ebb1","order_by":0,"name":"Fengxiang Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYJCCDwwGNnaM7Y2NDz8QqYNxBkNBWjJzz+FmYwnitXw4zNg+I71NgIcY9fIRyQebeQyYmXlnPmxjkGCwk9NtIKDF8EZaYuMMAzY+ydmJbQ8KGJKNzQ4Q0jIjx/zBBwMeZsPZie0GEgwHErcRocWwIcFAgnH/zYNtEjzEaJGXAGr5YGDA2DiDkUgtBjzPQH5JSGbsSQQGsgERfpFvB4XYn//AqDz+8OGHCjs5gloMUBUYEFAOtqWBCEWjYBSMglEwwgEAFFxE6SarYvMAAAAASUVORK5CYII=","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences), National Engineering and Technological Research Center of Marine Monitoring Equipment","correspondingAuthor":true,"prefix":"","firstName":"Fengxiang","middleName":"","lastName":"Guo","suffix":""},{"id":276363146,"identity":"4bf01df1-34b3-499e-bf89-312bffc4c654","order_by":1,"name":"Xinyun Guo","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences), National Engineering and Technological Research Center of Marine Monitoring Equipment","correspondingAuthor":false,"prefix":"","firstName":"Xinyun","middleName":"","lastName":"Guo","suffix":""},{"id":276363147,"identity":"aa29cb45-30c4-4620-912e-0f6a7b949c21","order_by":2,"name":"Lei Guo","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences), National Engineering and Technological Research Center of Marine Monitoring Equipment","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Guo","suffix":""},{"id":276363148,"identity":"0a58f878-fb7b-4301-aafc-c2e3d228ae22","order_by":3,"name":"Qinhang Wang","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences), National Engineering and Technological Research Center of Marine Monitoring Equipment","correspondingAuthor":false,"prefix":"","firstName":"Qinhang","middleName":"","lastName":"Wang","suffix":""},{"id":276363149,"identity":"0885ba66-bbfe-42f9-92b8-151a3407e598","order_by":4,"name":"Shousheng Liu","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences), National Engineering and Technological Research Center of Marine Monitoring Equipment","correspondingAuthor":false,"prefix":"","firstName":"Shousheng","middleName":"","lastName":"Liu","suffix":""},{"id":276363150,"identity":"36a93487-8770-4e2f-a80f-e6993c16a761","order_by":5,"name":"Mei Zhang","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences), National Engineering and Technological Research Center of Marine Monitoring Equipment","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Zhang","suffix":""},{"id":276363151,"identity":"09001934-ebc8-45dd-954b-3b28eadaaa51","order_by":6,"name":"Yan Liu","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences), National Engineering and Technological Research Center of Marine Monitoring Equipment","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Liu","suffix":""},{"id":276363152,"identity":"b49ffd55-347f-438c-bc9b-9cc28a42f708","order_by":7,"name":"Zhigang Gai","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences), National Engineering and Technological Research Center of Marine Monitoring Equipment","correspondingAuthor":false,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Gai","suffix":""}],"badges":[],"createdAt":"2024-02-17 06:31:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3963300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3963300/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54269935,"identity":"af274ed9-8e28-44b0-bddc-970bfc1172ce","added_by":"auto","created_at":"2024-04-08 06:28:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1481112,"visible":true,"origin":"","legend":"","description":"","filename":"TargetdetectionofdiamondnanostructuresbasedonimprovedYOLOv8modeling..pdf","url":"https://assets-eu.researchsquare.com/files/rs-3963300/v1_covered_c5f8c527-0fe1-43d3-b813-8c392aa09ad2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Target detection of diamond nanostructures based on improved YOLOv8 modeling","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":"YOLOv8, DCN_C2f, Shuffle Attention, WIoU, Diamond nanostructure","lastPublishedDoi":"10.21203/rs.3.rs-3963300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3963300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBoron-doped diamond thin films exhibit extensive applications in chemical sensing, in which functionalized nanostructures on the surfaces enhances further the performance of these films. However, targets detecting within such nanostructures faces great challenges such as noise, unclear object boundaries, and mutual occlusion, leading to inaccuracies in existing detection models. To tackle these challenges, we optimized the YOLOv8 model and introduced DWS-YOLOv8 for target detection of diamond nanostructures. The integration of the Deformable Convolutional C2f (DCN_C2f) module into the backbone network allowed adaptive adjustment of the network's receptive field. Moreover, incorporating the Shuffle Attention (SA) mechanism effectively addressed detail loss during convolutional iterations and reduced noise's impact on prediction results. Finally, leveraging Wise-IoU (WIoU) v3 as the bounding box regression loss enhanced the model's focus on diamond nanostructure samples, thereby improving localization capability. Experimental results showcase that compared to YOLOv8, our model achieves a 9.4% higher detection accuracy with reduced computational complexity. Furthermore, the recall rate (R) saw an increase of 0.6%, [email protected] improved by 2.6%, and [email protected]:0.95 increased by 0.6%. Additionally, DWS-YOLOv8 demonstrated enhancements in precision (P), recall (R), [email protected], and [email protected]:0.95, validating the effectiveness of our approach in enhancing target detection performance.\u003c/p\u003e","manuscriptTitle":"Target detection of diamond nanostructures based on improved YOLOv8 modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 07:04:46","doi":"10.21203/rs.3.rs-3963300/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"034ac722-b9c3-466f-8adc-18234622c4d5","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29129504,"name":"Biological sciences/Computational biology and bioinformatics/Image processing"},{"id":29129505,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"}],"tags":[],"updatedAt":"2024-04-08T06:20:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-05 07:04:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3963300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3963300","identity":"rs-3963300","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}