CSA-Lanenet: A Contiguous Spatial Attention Lane Detection Network with Vision Transformer Modules

CSA-Lanenet: A Contiguous Spatial Attention Lane Detection Network with Vision Transformer Modules | 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 CSA-Lanenet: A Contiguous Spatial Attention Lane Detection Network with Vision Transformer Modules Wei-Jong Yang, Li-Yang Ho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4538342/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2024 Read the published version in The Visual Computer → Version 1 posted 9 You are reading this latest preprint version Abstract Lane detection, which is one of the important technologies for automatic driving to effectively avoid the accidents caused by vehicles deviating from their driving lanes. The lane detection task is challenging due to complex scenes and few features in distorted lane lines. Therefore, collecting useful spatial information of lane lines associated with feature maps becomes an important task for precision lane line detection. Considering the image captured from the front car camera, in this paper, we use deep learning segmentation technologies to precisely extract the lane lines by using contiguous spatial attentions. We first propose the shortened spatial attention module into the lane detection network through the correlated spatial local information and transfer to adjacent features to improve lane detection performance. In addition, due to the successful improvement of vision transformers, we also introduce several simplified poolformers into the proposed lane detection network for further improving lane detection exclusively. Simulation results show that the proposed shortened spatial attention module and the simplified poolformer can achieve effective and accurate lane detection. (The code is available at https://github.com/jong12/CSA-Lanet .) Deep learning Autonomous driving Lane detection Image segmentation Spatial attention Vision transformer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2024 Read the published version in The Visual Computer → Version 1 posted Editorial decision: Revision requested 25 Sep, 2024 Reviews received at journal 24 Sep, 2024 Reviewers agreed at journal 12 Aug, 2024 Reviews received at journal 26 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers invited by journal 18 Jun, 2024 Editor assigned by journal 06 Jun, 2024 Submission checks completed at journal 06 Jun, 2024 First submitted to journal 06 Jun, 2024 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. 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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-4538342","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":316142227,"identity":"6aac187c-06af-4cd9-ab69-200a5309cc6d","order_by":0,"name":"Wei-Jong Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFCCAyDCBsphI15LGhAzQ7TwEGnVYRK0GBw8Y/i54Nf5PP4Z+QcYPpQdZrCXSCCg5cAZY+mZfbeLJW4kMzDOOHeYgYeQFrMDZwykeXtuJ26QSGZg5m0DapEmrMX4N2/POYiWv0RqMZPm+XEAooWRGC32B46VWfM2JCfOOPPY4GDPuXQenvsP8GuRnHF4822eP3aJ/e2JDx/8KLOWY+85gF8Lg8QJAwbGNggbpJaImORvB7rjD2F1o2AUjIJRMIIBAH66RxJPMHRRAAAAAElFTkSuQmCC","orcid":"","institution":"National Chin-Yi University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Wei-Jong","middleName":"","lastName":"Yang","suffix":""},{"id":316142228,"identity":"58566ba3-bd46-4217-9b20-29b35c5103cd","order_by":1,"name":"Li-Yang Ho","email":"","orcid":"","institution":"National Cheng Kung University","correspondingAuthor":false,"prefix":"","firstName":"Li-Yang","middleName":"","lastName":"Ho","suffix":""}],"badges":[],"createdAt":"2024-06-06 08:03:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4538342/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4538342/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00371-024-03731-6","type":"published","date":"2024-11-28T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70382727,"identity":"ca8c08c6-d9ae-4307-b658-36a175a2e72a","added_by":"auto","created_at":"2024-12-02 16:29:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":561088,"visible":true,"origin":"","legend":"","description":"","filename":"VisualComputer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4538342/v1_covered_09d2fe6a-bf4f-4e9f-ae7e-e9d028949985.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CSA-Lanenet: A Contiguous Spatial Attention Lane Detection Network with Vision Transformer Modules","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":"the-visual-computer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tvcj","sideBox":"Learn more about [The Visual Computer](http://link.springer.com/journal/371)","snPcode":"371","submissionUrl":"https://submission.nature.com/new-submission/371/3","title":"The Visual Computer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Deep learning, Autonomous driving, Lane detection, Image segmentation, Spatial attention, Vision transformer","lastPublishedDoi":"10.21203/rs.3.rs-4538342/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4538342/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLane detection, which is one of the important technologies for automatic driving to effectively avoid the accidents caused by vehicles deviating from their driving lanes. The lane detection task is challenging due to complex scenes and few features in distorted lane lines. Therefore, collecting useful spatial information of lane lines associated with feature maps becomes an important task for precision lane line detection. Considering the image captured from the front car camera, in this paper, we use deep learning segmentation technologies to precisely extract the lane lines by using contiguous spatial attentions. We first propose the shortened spatial attention module into the lane detection network through the correlated spatial local information and transfer to adjacent features to improve lane detection performance. In addition, due to the successful improvement of vision transformers, we also introduce several simplified poolformers into the proposed lane detection network for further improving lane detection exclusively. 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