Object Detection of Pedestrain and Vehicle at Night Based on Improved YOLO Algorithm

Object Detection of Pedestrain and Vehicle at Night Based on Improved YOLO Algorithm | 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 Object Detection of Pedestrain and Vehicle at Night Based on Improved YOLO Algorithm Li Zou, Yuting Zhang, Xinhua Yang, Yibo Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4495135/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 In the task of pedestrian and vehicle detection at night, challenges such as complex environments, small target sizes, and dense distributions exist. Aiming at these challeges, a novel YOLOv8-ECSH based on YOLOv8 is suggested in this research. Firstly, the lightweight up-sampling operator CARAFE is introduced. It aggregates information about the context with greater sense of wildness, thereby could enhance the algorithm's detection speed and accuracy. Secondly, the CIOU Loss is replaced by the EIOU Loss. This minimises the size of the difference in width and height between the predicted boxes and ground truth boxes, so that could enhance the speed of convergence of models. Thirdly, SPPCSPC module is inserted to backbone network to extend features in different scales of the target. Finally, one small target detection layer was added, while a large target detection layer is cropped out. It improves the detection of small targets by the model. The experimental results on a self-made dataset of pedestrian and vehicle at night show that the YOLOv8-ECSH model improves the recall by 4.1% and the [email protected] by 1.8% to 92.6%. Object detection YOLOv8 Feature fusion CARAFE Full Text Supplementary Files snarticle.blg 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-4495135","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489260070,"identity":"24757b97-0c23-4165-bc65-c36d32f7c609","order_by":0,"name":"Li Zou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYNCCAhsefvYGYlWzgQiDNBnJngOkaTlsY3DDgUgd8vN7DBh/GJznYbjBwPjhYw4RWgyO8RgwSBjc5mGc3cAsOXMbMVrYgFoMgFqYZQ6wMfMSo0W+DaglweAcD5tEApFaGEAOO2BwgIeHaC0Gx9IKGBsMknkkeA42E+cX+ebDGxh/VNjZ2x9vPvjhI1EOY2Bg/wGhGRuIUz8KRsEoGAWjgDAAAHEzLJwudYXBAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6724-8620","institution":"Dalian Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Zou","suffix":""},{"id":489260071,"identity":"e023bbb7-64e4-45f1-a922-c8b2b848d803","order_by":1,"name":"Yuting Zhang","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Zhang","suffix":""},{"id":489260072,"identity":"311e4304-1c46-4939-98d5-55dc452ac9de","order_by":2,"name":"Xinhua Yang","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xinhua","middleName":"","lastName":"Yang","suffix":""},{"id":489260073,"identity":"fd32b87f-ee66-4e7f-b7a6-aff02ae77829","order_by":3,"name":"Yibo Sun","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yibo","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-05-29 07:35:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4495135/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4495135/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89229279,"identity":"e4142b53-aa15-44b8-8684-bd5097e34bc3","added_by":"auto","created_at":"2025-08-17 14:04:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2100617,"visible":true,"origin":"","legend":"","description":"","filename":"SOCOD2403031reviewer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4495135/v1_covered_cbad41eb-9703-4ad9-a6dd-545ab82fc09c.pdf"},{"id":87565005,"identity":"97545171-a946-48d3-956c-d38c6576cc7d","added_by":"auto","created_at":"2025-07-25 09:22:12","extension":"blg","order_by":40,"title":"","display":"","copyAsset":false,"role":"supplement","size":2493,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.blg","url":"https://assets-eu.researchsquare.com/files/rs-4495135/v1/3586823a3862e6cddb26c798.blg"}],"financialInterests":"","formattedTitle":"Object Detection of Pedestrain and Vehicle at Night Based on Improved YOLO Algorithm","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":"Object detection, YOLOv8, Feature fusion, CARAFE","lastPublishedDoi":"10.21203/rs.3.rs-4495135/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4495135/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the task of pedestrian and vehicle detection at night, challenges such as complex environments, small target sizes, and dense distributions exist. 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