Robust Text Detection in Foggy Traffic Scenes Using an Enhanced CTPN Model with De-fogging Pre-processing

Robust Text Detection in Foggy Traffic Scenes Using an Enhanced CTPN Model with De-fogging Pre-processing | 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 Robust Text Detection in Foggy Traffic Scenes Using an Enhanced CTPN Model with De-fogging Pre-processing chang han, zhengqiang xiong, yingying liu, runmin wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7147426/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Text detection in natural scenes, particularly under challenging weather conditions like fog, remains a formidable task due to complex backgrounds, irregular text arrangements, and uneven illumination. This paper introduces a robust text detection method tailored for foggy traffic images, leveraging an improved Connectionist Text Proposal Network (CTPN) model. To enhance text clarity in foggy conditions, a defogging pre-processing step is incorporated, inspired by atmospheric scattering models. Additionally, a hybrid post-processing module combining NMS and Soft-NMS is proposed to refine text detection results, particularly for horizontal texts. Experimental evaluations on the ICDAR2013 and HSText-1000 datasets demonstrate the superior performance of our method, achieving recall, precision, and F-score improvements of 16.13%, 2.96%, and 10.16%, respectively, over the original CTPN model on the HSText-1000 dataset. This work provides a promising architecture for text detection in adverse weather conditions. Physical sciences/Engineering Physical sciences/Mathematics and computing Text Detection Traffic Images Enhanced CTPN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Oct, 2025 Reviews received at journal 05 Sep, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 19 Aug, 2025 Editor assigned by journal 19 Aug, 2025 Editor invited by journal 08 Aug, 2025 Submission checks completed at journal 02 Aug, 2025 First submitted to journal 02 Aug, 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-7147426","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504733301,"identity":"3df3f81c-a4f5-407d-816e-966a9c3079f1","order_by":0,"name":"chang han","email":"","orcid":"","institution":"Wuhan Business University","correspondingAuthor":false,"prefix":"","firstName":"chang","middleName":"","lastName":"han","suffix":""},{"id":504733303,"identity":"ea233614-f5c9-433e-9b0c-519b93588e7c","order_by":1,"name":"zhengqiang xiong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYJCCA0AsxyABYrKRoMWYNC0gkNhAtBb59hzDAx931KZvuN1jwPCh7DAD/+wG/FoYe54lHJx55njuhjtnDBhnnDvMIHHnAH4tzBLJBw7zth3L3XYjx4CZt+0wg4FEAn4tbBKJDSAt6WYgLX+J0cIDsaUmAayFkRgtEjwgv7QdMNx/51jBwZ5z6TwSNwhoAYaY8YePbXXykrObNz74UWYtxz+DgBYGBrCCw2DmAZBLCamHaakjQuEoGAWjYBSMWAAA3qRHh4YHif0AAAAASUVORK5CYII=","orcid":"","institution":"Wuhan Business University","correspondingAuthor":true,"prefix":"","firstName":"zhengqiang","middleName":"","lastName":"xiong","suffix":""},{"id":504733305,"identity":"65023fef-fc0a-4d1f-ad2a-91ae83f67c1d","order_by":2,"name":"yingying liu","email":"","orcid":"","institution":"Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"yingying","middleName":"","lastName":"liu","suffix":""},{"id":504733309,"identity":"10f3691e-f325-482e-9878-0710695caec4","order_by":3,"name":"runmin wang","email":"","orcid":"","institution":"Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"runmin","middleName":"","lastName":"wang","suffix":""}],"badges":[],"createdAt":"2025-07-17 09:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7147426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7147426/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-43357-3","type":"published","date":"2026-03-13T15:58:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104740835,"identity":"3971e23d-0e4b-4159-8f7d-9e034e0ece3a","added_by":"auto","created_at":"2026-03-16 16:19:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":852568,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript4revised1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7147426/v1_covered_bbf034ea-6d83-47ff-9709-e961dfab2025.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Text Detection in Foggy Traffic Scenes Using an Enhanced CTPN Model with De-fogging Pre-processing","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Text Detection, Traffic Images, Enhanced CTPN","lastPublishedDoi":"10.21203/rs.3.rs-7147426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7147426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eText detection in natural scenes, particularly under challenging weather conditions like fog, remains a formidable task due to complex backgrounds, irregular text arrangements, and uneven illumination. 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