CPMS-Net :  A Lightweight Cooperative Perception Multi-Scale Network for Crowd Counting

CPMS-Net : A Lightweight Cooperative Perception Multi-Scale Network for Crowd Counting | 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 CPMS-Net : A Lightweight Cooperative Perception Multi-Scale Network for Crowd Counting Gufeng Shang, Huqin Weng, Xuming Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9521929/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Crowd counting aims to estimate the number of people in an image and is a key technology in applications such as intelligent surveillance and public security. With the development of edge computing, deploying crowd counting models on resource-constrained edge devices has become a significant trend, which poses higher requirements for model accuracy, parameter scale, and inference efficiency. However, existing methods universally face a trade-off between performance and complexity: on the one hand, although heavy models possess strong feature representation capabilities, they incur high computational overhead and are difficult to deploy; on the other hand, lightweight models suffer from significant performance degradation in scenarios with complex backgrounds and drastic scale variations due to their limited receptive fields. To address these issues, this paper proposes a lightweight Cooperative Perception Multi-Scale Network (CPMS-Net). Specifically, a Cooperative Perception Module (CPM) is first introduced in the feature extraction stage to simultaneously model inter-channel relationships and spatial positional information. Subsequently, a Multi-Scale Optimization Module (MSOM) is introduced to enhance the model's adaptability to scale variations. Finally, a Lightweight Residual Attention Module (LRAM) is constructed to capture long-range dependencies at a low computational cost. Experimental results on multiple public datasets demonstrate that CPMS-Net, with an ultra-low parameter count of only 0.12M, achieves an MAE of 126.7 and an MSE of 201.0 on the UCF-QNRF dataset. This fully demonstrates its excellent trade-off between accuracy and computational efficiency, validating its application potential for edge device deployment. Crowd Counting Lightweight Network Cooperative Perception Multi-Scale Optimization Edge Computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 29 Apr, 2026 Editor assigned by journal 27 Apr, 2026 Submission checks completed at journal 27 Apr, 2026 First submitted to journal 24 Apr, 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-9521929","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634965912,"identity":"43c21832-9d6e-45f0-b78e-baf8e07eda39","order_by":0,"name":"Gufeng Shang","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Gufeng","middleName":"","lastName":"Shang","suffix":""},{"id":634965913,"identity":"62df689b-1544-4ca3-99d0-b1ea5ece3ad6","order_by":1,"name":"Huqin Weng","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Huqin","middleName":"","lastName":"Weng","suffix":""},{"id":634965918,"identity":"e1e5d893-bfab-472a-9428-9467ef23ea97","order_by":2,"name":"Xuming Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYBACPjBpwMDAz8x88AFRWthgWiTb2ZINSNAC0nWex0yCOC0SOWZSNwru2G0+zJYmwVBzhxgtaWnSOQbPkrcdZj5swXDsGTFako8BtRxONjvMlniDseEwMVoS28BajJt5DCSI1AKxxc6AmceISC08z5KtgVoSJA4DAznhGBFa+NlzDG/n/Dlsz99/+OCDDzVEaIGBxAYQmUC8BgYGe1IUj4JRMApGwQgDAIoiNFXVoDt1AAAAAElFTkSuQmCC","orcid":"","institution":"Wuyi University","correspondingAuthor":true,"prefix":"","firstName":"Xuming","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2026-04-25 03:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9521929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9521929/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108661274,"identity":"828047a3-b2aa-4a1c-a56f-a79074d81164","added_by":"auto","created_at":"2026-05-07 05:25:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":574154,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9521929/v1_covered_7e2444ad-1050-44c8-a65c-b30a43810360.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CPMS-Net : A Lightweight Cooperative Perception Multi-Scale Network for Crowd Counting","fulltext":[],"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":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-king-saud-university-computer-and-information-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of King Saud University Computer and Information Sciences](https://link.springer.com/journal/44443)","snPcode":"44443","submissionUrl":"https://submission.springernature.com/new-submission/44443/3","title":"Journal of King Saud University Computer and Information Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Crowd Counting, Lightweight Network, Cooperative Perception, Multi-Scale Optimization, Edge Computing","lastPublishedDoi":"10.21203/rs.3.rs-9521929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9521929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCrowd counting aims to estimate the number of people in an image and is a key technology in applications such as intelligent surveillance and public security. 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Subsequently, a Multi-Scale Optimization Module (MSOM) is introduced to enhance the model's adaptability to scale variations. Finally, a Lightweight Residual Attention Module (LRAM) is constructed to capture long-range dependencies at a low computational cost. Experimental results on multiple public datasets demonstrate that CPMS-Net, with an ultra-low parameter count of only 0.12M, achieves an MAE of 126.7 and an MSE of 201.0 on the UCF-QNRF dataset. 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