AHOD: Adaptive Hybrid Object Detector for Context-Aware and real-time object detection in complex environments

AHOD: Adaptive Hybrid Object Detector for Context-Aware and real-time object detection in complex environments | 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 AHOD: Adaptive Hybrid Object Detector for Context-Aware and real-time object detection in complex environments Serge STephane AMAN, Tiemoman KONE, Behou Gerard N'GUESSAN, Kouadio Prosper KIMOU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6791387/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This article introduces Adaptive Hybrid Object Detector (AHOD) , a new paradigm in object detection designed to combine speed, accuracy and contextual adaptability. Current models, such as YOLO and Faster R-CNN, have significant limitations: YOLO excels in speed but often fails on complex objects, while Faster R-CNN prioritizes accuracy at the expense of inference time. AHOD is based on three major innovations: Feature Pyramid Enhancement (FPE ), which improves multi-scale detection; Dynamic Context Module (DCM ), which dynamically adjusts features according to context; and Fast and Accurate Detection Head (FADH ), which balances speed and accuracy. Experimental results on COCO and Pascal VOC datasets show that AHOD outperforms existing models with an average accuracy (mAP) increase of 7%, while reducing inference time by 30%. These results demonstrate the potential of AHOD for real-time critical applications such as autonomous vehicles and surveillance. Object detection Real-time processing Deep Learning Adaptive hybrid model Context-aware AI Adaptive hybrid object detector (AHOD) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Jun, 2025 Reviews received at journal 25 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviews received at journal 16 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers invited by journal 03 Jun, 2025 Editor assigned by journal 03 Jun, 2025 Submission checks completed at journal 03 Jun, 2025 First submitted to journal 31 May, 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-6791387","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466453186,"identity":"0075a084-fd06-4d3b-8be3-7352eaab83da","order_by":0,"name":"Serge STephane AMAN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYHAD5gNAQgKIE4DYgCgtbAkka+GBKUvAr06+vffxiw8MdtH8s3u+SfPUWDDws+cYMPwowK3F4MxxM8sZDMm5M+6c3SbNc0yCQbLnjQFjDx6HGUiksRnzMDDnNtzI3SY5s0GCweBGjgEzPr/Iz3/GZvyHoT53/o2cZ2At9oS0MNxgY37MwHA4d8ONHDaJjyBbJAhoMTiTxgZ0+fHcjTfSjC0+HJPgkTjzrOAgPr/Itx9j/vCjojp33o3khzcSaurk+NuTNz748QePw4BRKIHsDB4QcQCvBmBC+UBAwSgYBaNgFIx0AADvX0rFCyZB4gAAAABJRU5ErkJggg==","orcid":"","institution":"Virtual University of Côte d'Ivoire","correspondingAuthor":true,"prefix":"","firstName":"Serge","middleName":"STephane","lastName":"AMAN","suffix":""},{"id":466453188,"identity":"64f4d465-edac-4692-894d-c1f91a94c163","order_by":1,"name":"Tiemoman KONE","email":"","orcid":"","institution":"Virtual University of Côte d'Ivoire","correspondingAuthor":false,"prefix":"","firstName":"Tiemoman","middleName":"","lastName":"KONE","suffix":""},{"id":466453190,"identity":"80da2acd-493b-4198-8856-c1c4498bee6e","order_by":2,"name":"Behou Gerard N'GUESSAN","email":"","orcid":"","institution":"Virtual University of Côte d'Ivoire","correspondingAuthor":false,"prefix":"","firstName":"Behou","middleName":"Gerard","lastName":"N'GUESSAN","suffix":""},{"id":466453191,"identity":"89f4dda0-781d-43ee-a656-8e5460ce65da","order_by":3,"name":"Kouadio Prosper KIMOU","email":"","orcid":"","institution":"Université Félix Houphouët-Boigny","correspondingAuthor":false,"prefix":"","firstName":"Kouadio","middleName":"Prosper","lastName":"KIMOU","suffix":""}],"badges":[],"createdAt":"2025-05-31 14:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6791387/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6791387/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83971198,"identity":"5c902bd8-22f8-402f-9b4b-471a8c5fcd30","added_by":"auto","created_at":"2025-06-05 08:00:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":700157,"visible":true,"origin":"","legend":"","description":"","filename":"AHODAdaptiveHybridObjectDetectorenGB.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6791387/v1_covered_8559e21d-4b59-4746-af65-92606230c830.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AHOD: Adaptive Hybrid Object Detector for Context-Aware and real-time object detection in complex environments","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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Object detection, Real-time processing, Deep Learning, Adaptive hybrid model, Context-aware AI, Adaptive hybrid object detector (AHOD)","lastPublishedDoi":"10.21203/rs.3.rs-6791387/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6791387/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article introduces \u003cb\u003eAdaptive Hybrid Object Detector (AHOD)\u003c/b\u003e, a new paradigm in object detection designed to combine speed, accuracy and contextual adaptability. 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