Cross-Modal Object Detection from UAVPerspectives with Frequency Domain Fusion andGated Feature Enhancement

Cross-Modal Object Detection from UAVPerspectives with Frequency Domain Fusion andGated Feature Enhancement | 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 Cross-Modal Object Detection from UAVPerspectives with Frequency Domain Fusion andGated Feature Enhancement Guangqiu Chen, Chengcai Guan, Fengming Liu, Sai Zhang, Guofeng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8755020/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Drones can capture wide-area and multi-angle images from the air and are widely used in the field of target detection. However, in bad weather and complex lighting conditions, traditional single-spectrum visible light imaging is difficult to obtain effective features of the target. When detecting small and dense targets, it is prone to false detection or missed detection. This paper proposes a cross-modal object detection algorithm that combines frequency-domain fusion and gated feature enhancement. Based on the YOLOv12 backbone network, a dual-branch feature fusion network is constructed to extract the feature information of visible light images and infrared images respectively.Dynamic Fusion Mechanism of Frequency-Domain Differences (DFF) is designed to retain the high-frequency and low-frequency information of the images. By emphasizing the spatial dimension differences between the two spectral features, deep feature fusion is achieved; To enhance the detection efficiency, lightweight Scharr Gated Feature Enhancement (SGFE) is designed to replace the corresponding module in the neck network. Our experiments on the DroneVehicle dataset demonstrated the advantages of our method and the effectiveness of MEF-Net in enhancing multi-spectral feature extraction for ground detection by unmanned aerial vehicles. Multimodal Feature fusion Frequency domain enhancement Small target detection drone Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 02 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 01 Feb, 2026 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-8755020","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584690756,"identity":"206d2c4f-4f3f-4598-9a9e-94553ee4822c","order_by":0,"name":"Guangqiu Chen","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Guangqiu","middleName":"","lastName":"Chen","suffix":""},{"id":584690757,"identity":"55db60f7-dd0b-482c-a303-d877edf5ed93","order_by":1,"name":"Chengcai Guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDACCQjFA6EqJOT4SdRyxsJYsoFILRDA2FaRuIGQFv7Zzccefqmxk2HgX2P4uXCeBOMGBuaHj27gs+TOsXRjmWPJPAwSb4ylZ26TYDZnYDM2zsGjxUAix0xasoEZqOWMgTTvNgk2ywYeNmn8WvK/AbXUg7QY/+adI8FjcICglhw2yY8Nh3kY+HvMpHkbJCQIapG4kWYmzXDsONAWtjJrnmMSBpLNBPzCPyP5meSPmmp7Bv7Dm2/z1NTV97M3P3yMTwsIMIPi0f5GAoxLQDkIMP4A23eACKWjYBSMglEwIgEAE1Q/3BJkIscAAAAASUVORK5CYII=","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Chengcai","middleName":"","lastName":"Guan","suffix":""},{"id":584690758,"identity":"4e3270e0-7f2c-4996-9ab6-316e81c5cc40","order_by":2,"name":"Fengming Liu","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fengming","middleName":"","lastName":"Liu","suffix":""},{"id":584690759,"identity":"acb39ea4-a30d-43b3-a6d5-e74c95bd179e","order_by":3,"name":"Sai Zhang","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sai","middleName":"","lastName":"Zhang","suffix":""},{"id":584690760,"identity":"bda8a759-e73e-4be1-aecd-ccfe1b0bef38","order_by":4,"name":"Guofeng Liu","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Guofeng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-02-01 09:09:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8755020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8755020/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102746824,"identity":"a2080539-f136-44d8-85f7-91399f357533","added_by":"auto","created_at":"2026-02-16 09:01:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":842092,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleTitle14.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8755020/v1_covered_06e84306-01ff-49fe-8967-daccc56b02fc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cross-Modal Object Detection from UAVPerspectives with Frequency Domain Fusion andGated Feature Enhancement","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multimodal Feature, fusion Frequency domain enhancement, Small target detection ,drone","lastPublishedDoi":"10.21203/rs.3.rs-8755020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8755020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Drones can capture wide-area and multi-angle images from the air and are widely used in the field of target detection. However, in bad weather and complex lighting conditions, traditional single-spectrum visible light imaging is difficult to obtain effective features of the target. When detecting small and dense targets, it is prone to false detection or missed detection. This paper proposes a cross-modal object detection algorithm that combines frequency-domain fusion and gated feature enhancement. Based on the YOLOv12 backbone network, a dual-branch feature fusion network is constructed to extract the feature information of visible light images and infrared images respectively.Dynamic Fusion Mechanism of Frequency-Domain Differences (DFF) is designed to retain the high-frequency and low-frequency information of the images. By emphasizing the spatial dimension differences between the two spectral features, deep feature fusion is achieved; To enhance the detection efficiency, lightweight Scharr Gated Feature Enhancement (SGFE) is designed to replace the corresponding module in the neck network. Our experiments on the DroneVehicle dataset demonstrated the advantages of our method and the effectiveness of MEF-Net in enhancing multi-spectral feature extraction for ground detection by unmanned aerial vehicles.","manuscriptTitle":"Cross-Modal Object Detection from UAVPerspectives with Frequency Domain Fusion andGated Feature Enhancement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 02:54:45","doi":"10.21203/rs.3.rs-8755020/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-25T03:37:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T02:06:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T02:04:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Signal, Image and Video Processing","date":"2026-02-01T09:06:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"19113c54-cfe4-43ad-92c0-c31817bb83a6","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T03:53:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 02:54:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8755020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8755020","identity":"rs-8755020","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}