Nighttime Vehicle Detection using Improved CycleGAN

Nighttime Vehicle Detection using Improved CycleGAN | 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 Nighttime Vehicle Detection using Improved CycleGAN Sathananthavathi V, Kandasamy K, Rajamanickam D This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1771621/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Nighttime Vehicle Detection is the challenging task to be addressed properly in the Automatic Driving System. Detection of oncoming vehicles in time is essential for safe driving in the Automatic Driving System and this task is critical when the lighting condition is deprived. Detecting vehicle in nighttime is based on the high-level features by the automotive system. In order to address the problem of nighttime vehicle detection effectively, image translation by improved Cycle Generative Adversarial Network (GAN) architecture is proposed in this paper. The Cycle Generative Adversarial Network (CycleGAN) is the deep convolution neural network trained in the form of image-to-image translation tasks. Cycle GAN contains two GANs, first GAN is used for night to day translation and second GAN for day to night translation. The proposed improved Cycle GAN architecture is enriched with shallow Unet Generator in the first image translator GAN. Using this architecture, the input night time image is getting translated into day time image and hence supporting to extract high level of features for the vehicle detection module to detect the vehicles with high confidence probability. The proposed architecture is compared with the conventional cycle GAN architecture in terms of the vehicle detection accuracy. From the experimental results, it is observed that proposed improved Cycle GAN can able to detect vehicles in the perplex view condition. GAN Night time Vehicle detection Unet Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 23 Jun, 2022 Editor assigned by journal 21 Jun, 2022 First submitted to journal 21 Jun, 2022 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-1771621","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":115876676,"identity":"b81c1c23-0871-4981-9c69-0f18bcda6468","order_by":0,"name":"Sathananthavathi V","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYHACNgglASYP8PCDqIQCfDqYIVp4wFoSDvBINoBoAxK0MBgcADHwaJFvP3/swc8ddnn20s3PJH7+uCNjfH514ocHBgzy/GIHsGoxOJPMbth7JrmYR+aYmWRPwjMesxtvN0sAHWY4c3YCdi0MyWwSvG3MiT0SCcYGPAmHgVrObgBpSTC4jV2LfP9jNsm/bfVALemfDf8AtRjPOLv5Bz4tDDeS2aR52w4DteQYPgbZYsDfuw2vLQY3HptJy7YdT+y5kVP4WCbtMI/EDd5tFgkGEjj9It+f+EzybVt1YvuM9A0H39gctufvP7v55o8KG3l+aRwOwwQSYJUSxCoHAf4DpKgeBaNgFIyCEQAASlNgumXNuU4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8732-8652","institution":"Mepco Schlenk Engineering College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sathananthavathi","middleName":"","lastName":"V","suffix":""},{"id":115876677,"identity":"4482723f-c2d2-46f3-a4b2-c08077451a41","order_by":1,"name":"Kandasamy K","email":"","orcid":"","institution":"Mepco Schlenk Engineering College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kandasamy","middleName":"","lastName":"K","suffix":""},{"id":115876678,"identity":"b23f9715-9d83-4d1a-9971-ef22631c3909","order_by":2,"name":"Rajamanickam D","email":"","orcid":"","institution":"Mepco Schlenk Engineering College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rajamanickam","middleName":"","lastName":"D","suffix":""}],"badges":[],"createdAt":"2022-06-18 14:16:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1771621/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1771621/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23342773,"identity":"f28edaff-2b87-4314-9bf2-c8587d1a8681","added_by":"auto","created_at":"2022-07-01 19:43:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":924054,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1771621/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Nighttime Vehicle Detection using Improved CycleGAN","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1771621/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wireless-personal-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wire","sideBox":"Learn more about [Wireless Personal Communications](https://www.springer.com/journal/11277)","snPcode":"11277","submissionUrl":"https://submission.nature.com/new-submission/11277/3","title":"Wireless Personal Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"GAN, Night time, Vehicle detection, Unet","lastPublishedDoi":"10.21203/rs.3.rs-1771621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1771621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Nighttime Vehicle Detection is the challenging task to be addressed properly in the Automatic Driving System. Detection of oncoming vehicles in time is essential for safe driving in the Automatic Driving System and this task is critical when the lighting condition is deprived. Detecting vehicle in nighttime is based on the high-level features by the automotive system. In order to address the problem of nighttime vehicle detection effectively, image translation by improved Cycle Generative Adversarial Network (GAN) architecture is proposed in this paper. The Cycle Generative Adversarial Network (CycleGAN) is the deep convolution neural network trained in the form of image-to-image translation tasks. Cycle GAN contains two GANs, first GAN is used for night to day translation and second GAN for day to night translation. The proposed improved Cycle GAN architecture is enriched with shallow Unet Generator in the first image translator GAN. 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