DCD-YOLO: An Improved YOLOv11n Algorithm \for Traffic Participant Detection

DCD-YOLO: An Improved YOLOv11n Algorithm \for Traffic Participant Detection | 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 DCD-YOLO: An Improved YOLOv11n Algorithm \for Traffic Participant Detection Xiaohui Lu, Dexin Wang, Ruixia Xiong, Xinzhan Lv, Yichong Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7771542/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 Detection of traffic participants is one of the core tasks in environmental perception for autonomous driving. However, accurately identifying small sized and occluded objects in complex traffic scenarios remains a pressing challenge. To address these issues, a high precision multi-scale perception architecture named DCD-YOLO is proposed based on YOLOv11n, which integrates multi-branch feature extraction, attention guided enhancement, and task decoupled prediction mechanisms.A Dynamic Fusion Block (DFB) is introduced to partially replace the original C3k2 structure, enhancing feature extraction for small objects and complex backgrounds. In addition, the Content Guided Attention (CGA) module is incorporated in the Neck to emphasize key object regions through the collaborative effects of channel, spatial, and pixel level attentions.Meanwhile, a Dynamic Shared Enhanced Head (DSEH) combines reparameterizable detail enhanced convolutions, shared convolutional weights, and Group Normalization to improve multi-scale object localization and classification performance while maintaining a lightweight architecture.Experimental results demonstrate that DCD-YOLO improves [email protected] by 2.9% on the KITTI dataset. Furthermore, the proposed model is deployed on a real vehicle platform and tested under realistic driving scenarios, which further verifies its deployment feasibility and practical significance for autonomous driving systems. YOLO Deep Learning Object Detection Traffic Participants 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-7771542","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533085862,"identity":"f298d141-4392-4c7a-a3c4-be865a7dfff9","order_by":0,"name":"Xiaohui Lu","email":"","orcid":"","institution":"Changchun University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Lu","suffix":""},{"id":533085863,"identity":"c6aac37f-c29c-431d-8fb8-eb2466348758","order_by":1,"name":"Dexin Wang","email":"","orcid":"","institution":"Changchun University of 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