Akhat-DETR: End-to-End Object Detection Model on Hazy Scenarios in Autonomous Driving

Akhat-DETR: End-to-End Object Detection Model on Hazy Scenarios in Autonomous Driving | 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 Akhat-DETR: End-to-End Object Detection Model on Hazy Scenarios in Autonomous Driving Zhao Liu, Zhiwei Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8581207/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 The innovative DEtection TRansformer(DETR) approach introduces the transformer encoder and decoder architecture into object detection, obviating the need for hand-designed components. Though modern detectors have attained competitive results on public dataset such as COCO dataset, their capabilities are perverted on images captured in inclement weather. In this paper, we propose Akhat-DETR, an end-to-end transformer-based detector designed for hazy scenes. First, we design a light-weight convolutional dehazing network which can be integrated seamlessly into detectors. Moreover, we design a novel one-size-fits-all feature fusion module named FFTA. In the end, a general supervised learning design paradigm is given: as long as the final annotations are available, intermediate annotations are dispensable, thus the end-to-end model can perform training and inference in its entirety. Akhat-DETR achieves 61.0% AP on RTTS dataset with a 3090 GPU, triumphing over state-of-the-art detectors. Codes of proposed modules, splitted dataset in COCO format and pre-trained models are available at https://github.com/ChizkiyahuOhayon/Akhat-DETR. Object Detection DETR Dehazing Algorithm Full Text Additional Declarations No competing interests reported. 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-8581207","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590634210,"identity":"56312beb-ba8d-4623-a86a-c35ea59dc354","order_by":0,"name":"Zhao Liu","email":"","orcid":"","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Liu","suffix":""},{"id":590634211,"identity":"77ee6481-3aec-40c5-a687-6a81d933ee7d","order_by":1,"name":"Zhiwei Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACPmYGBmYGAwYGNhBPosIGIsyDRwsbihaLM2lEaGEAaYGByrbDRGhh5zH+XFBwWI6Pgf3xhxts5xPn9x9gfPC2jUHeHKfDeAyMZxgcNmZj4DEwnMFzO7Gx4QCz4dw2BsOdDbi1JPMY3E5sAzomWULidmIzYwObNG8bQ4LBAdxaDgO11LcxsD84/MfgXGIbMwP7bwJaDJuBWhKA4WDYIJFwILGHDSiIXwtbMdCi/4ZAhxkzSBxINp7Bw9gsOeechOEGHFr4+Q9v/szzJ01evgEYYpL/7GTn9x8++OFNmY08LlsQQP4BjMXYACQkCKkfBaNgFIyCUYAHAACMQ0rne3eUWAAAAABJRU5ErkJggg==","orcid":"","institution":"Zaozhuang University","correspondingAuthor":true,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-01-12 11:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8581207/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8581207/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105033852,"identity":"ec15086d-87df-4757-b21d-3fa78f588752","added_by":"auto","created_at":"2026-03-20 07:21:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1094198,"visible":true,"origin":"","legend":"","description":"","filename":"AkhatDETR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8581207/v1_covered_2d710881-8720-4c67-b767-41fe4861011b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Akhat-DETR: End-to-End Object Detection Model on Hazy Scenarios in Autonomous Driving","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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, DETR, Dehazing Algorithm","lastPublishedDoi":"10.21203/rs.3.rs-8581207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8581207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe innovative DEtection TRansformer(DETR) approach introduces the transformer encoder and decoder architecture into object detection, obviating the need for hand-designed components. 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