PGCF-YOLO: a railway fastener detection algorithm based on improved YOLOv8n

PGCF-YOLO: a railway fastener detection algorithm based on improved YOLOv8n | 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 PGCF-YOLO: a railway fastener detection algorithm based on improved YOLOv8n Siwei Ma, Ronghua Li, Henan Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6518914/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Sep, 2025 Read the published version in Journal of Real-Time Image Processing → Version 1 posted 13 You are reading this latest preprint version Abstract Railway fasteners are critical components for maintaining track stability and ensuring the safe operation of trains. Accurate and real-time detection of fastener defects is essential for achieving intelligent railway maintenance. However, existing object detection algorithms often struggle to balance detection accuracy, computational efficiency. To address these challenges, this paper proposes an improved lightweight detection algorithm—PGCF-YOLO (Pyramid Split Attention - GSConv + VoV-GSCSP – CARAFE – FocalerIoU – MPDIoU – You Only Look Once), based on the YOLOv8n architecture, aimed at enhancing the overall performance of the model. First, a Pyramid Split Attention (PSA) module is integrated into the backbone to strengthen the model’s capability in perceiving complex defect features. Then, lightweight GSConv and VoV-GSCSP modules are introduced into the Neck to reduce parameter count and computational overhead while maintaining strong feature extraction capacity. The CARAFE upsampling operator is adopted to replace traditional nearest-neighbor interpolation, improving the model’s ability to capture both fine-grained details and global semantic information. Finally, a novel regression loss function, Focaler-MPDIoU, is proposed to enhance bounding box localization accuracy and accelerate training convergence. Experimental results demonstrate that PGCF-YOLO achieves excellent inference efficiency while maintaining high detection accuracy, reaching 99.3% mAP and 128 FPS, which represent improvements of 2.2% in accuracy and 12 FPS in speed over the original YOLOv8n. Furthermore, the parameter count and GFLOPs are reduced by 11.0% and 17.3%, respectively. Compared with other mainstream object detection models, PGCF-YOLO demonstrates superior performance in detection accuracy, inference speed, and model size. Experimental results on the E-Type fastener dataset demonstrate that PGCF-YOLO possesses strong generalization capability, validating its practical applicability in railway fastener defect detection tasks. Railway fastener defect detection YOLOv8n PSA attention mechanism GSConv CARAFE Focaler-MPDIoU loss function Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Sep, 2025 Read the published version in Journal of Real-Time Image Processing → Version 1 posted Editorial decision: Revision requested 16 May, 2025 Reviews received at journal 16 May, 2025 Reviews received at journal 16 May, 2025 Reviews received at journal 15 May, 2025 Reviews received at journal 15 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 25 Apr, 2025 Submission checks completed at journal 25 Apr, 2025 First submitted to journal 24 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. 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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-6518914","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450242659,"identity":"1dc02c0a-ff65-4b33-a420-09ccab39e292","order_by":0,"name":"Siwei Ma","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Siwei","middleName":"","lastName":"Ma","suffix":""},{"id":450242661,"identity":"d51d13e4-8181-4eea-900f-d236c6938b50","order_by":1,"name":"Ronghua Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3PMQrCMBSA4RcC6ZLaVSjiFSIBdSjepRQyOXiEuOhSdK2LXkFvoD7s5AE6OCgF544iHZQO4tR0FMy/ZHkfeQ/AZvvBWrR69gDd27woRDAyEvYhEKUkmajITOBDlKK8OBJtJI57yyfPS7jU534eiD0FB0/b+sUcKVeLe5gc4oEci0sLuFJZPWHMd2MMNXLhj8WdQpv3m5FN+iZDgUQ3IvyB4fbMlA8NCZWuRrnLKPZioSJmusXzUpLzEjvrjEyvjzIYeQ6mtaSKzL7/NY5Xlc3GbDab7U97AS3gSApb8W5ZAAAAAElFTkSuQmCC","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Ronghua","middleName":"","lastName":"Li","suffix":""},{"id":450242664,"identity":"0ce0140c-207c-4b6f-9784-ff0fd9eceed5","order_by":2,"name":"Henan Hu","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Henan","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-04-24 08:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6518914/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6518914/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11554-025-01762-3","type":"published","date":"2025-09-12T15:57:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91817655,"identity":"992de749-8cd3-474c-b180-8189c61f1b92","added_by":"auto","created_at":"2025-09-22 07:00:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1116979,"visible":true,"origin":"","legend":"","description":"","filename":"PGCFYOLOarailwayfastenerdetectionalgorithmbasedonimprovedYOLOv8n.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6518914/v1_covered_959d13ae-2c84-444f-8cb7-d44530fd0ee9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PGCF-YOLO: a railway fastener detection algorithm based on improved YOLOv8n","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-real-time-image-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rtip","sideBox":"Learn more about [Journal of Real-Time Image Processing](http://link.springer.com/journal/11554)","snPcode":"11554","submissionUrl":"https://submission.nature.com/new-submission/11554/3","title":"Journal of Real-Time Image Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Railway fastener defect detection, YOLOv8n, PSA attention mechanism, GSConv, CARAFE, Focaler-MPDIoU loss function","lastPublishedDoi":"10.21203/rs.3.rs-6518914/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6518914/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRailway fasteners are critical components for maintaining track stability and ensuring the safe operation of trains. Accurate and real-time detection of fastener defects is essential for achieving intelligent railway maintenance. However, existing object detection algorithms often struggle to balance detection accuracy, computational efficiency. To address these challenges, this paper proposes an improved lightweight detection algorithm\u0026mdash;PGCF-YOLO (Pyramid Split Attention - GSConv\u0026thinsp;+\u0026thinsp;VoV-GSCSP \u0026ndash; CARAFE \u0026ndash; FocalerIoU \u0026ndash; MPDIoU \u0026ndash; You Only Look Once), based on the YOLOv8n architecture, aimed at enhancing the overall performance of the model. First, a Pyramid Split Attention (PSA) module is integrated into the backbone to strengthen the model\u0026rsquo;s capability in perceiving complex defect features. Then, lightweight GSConv and VoV-GSCSP modules are introduced into the Neck to reduce parameter count and computational overhead while maintaining strong feature extraction capacity. The CARAFE upsampling operator is adopted to replace traditional nearest-neighbor interpolation, improving the model\u0026rsquo;s ability to capture both fine-grained details and global semantic information. Finally, a novel regression loss function, Focaler-MPDIoU, is proposed to enhance bounding box localization accuracy and accelerate training convergence. Experimental results demonstrate that PGCF-YOLO achieves excellent inference efficiency while maintaining high detection accuracy, reaching 99.3% mAP and 128 FPS, which represent improvements of 2.2% in accuracy and 12 FPS in speed over the original YOLOv8n. Furthermore, the parameter count and GFLOPs are reduced by 11.0% and 17.3%, respectively. Compared with other mainstream object detection models, PGCF-YOLO demonstrates superior performance in detection accuracy, inference speed, and model size. Experimental results on the E-Type fastener dataset demonstrate that PGCF-YOLO possesses strong generalization capability, validating its practical applicability in railway fastener defect detection tasks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"PGCF-YOLO: a railway fastener detection algorithm based on improved YOLOv8n","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-02 05:28:05","doi":"10.21203/rs.3.rs-6518914/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-16T11:55:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T08:17:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T07:21:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T14:06:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T09:31:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313158140738794939077807656873985655641","date":"2025-05-08T07:07:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19641665446023432235329922475130109964","date":"2025-05-06T07:55:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338827646218396922208038556976419677057","date":"2025-05-06T07:49:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80548876932136472319938504252397150003","date":"2025-04-29T03:27:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-28T08:08:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-25T12:07:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T12:04:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Real-Time Image Processing","date":"2025-04-24T08:42:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-real-time-image-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rtip","sideBox":"Learn more about [Journal of Real-Time Image Processing](http://link.springer.com/journal/11554)","snPcode":"11554","submissionUrl":"https://submission.nature.com/new-submission/11554/3","title":"Journal of Real-Time Image Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"01e3be81-292f-4453-925c-35eb7852f4b9","owner":[],"postedDate":"May 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T06:51:46+00:00","versionOfRecord":{"articleIdentity":"rs-6518914","link":"https://doi.org/10.1007/s11554-025-01762-3","journal":{"identity":"journal-of-real-time-image-processing","isVorOnly":false,"title":"Journal of Real-Time Image Processing"},"publishedOn":"2025-09-12 15:57:03","publishedOnDateReadable":"September 12th, 2025"},"versionCreatedAt":"2025-05-02 05:28:05","video":"","vorDoi":"10.1007/s11554-025-01762-3","vorDoiUrl":"https://doi.org/10.1007/s11554-025-01762-3","workflowStages":[]},"version":"v1","identity":"rs-6518914","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6518914","identity":"rs-6518914","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}