Multi-Target Tracking Algorithm for Floating Garbage on Water Surfaces Based on Unmanned Surface Vessels

Multi-Target Tracking Algorithm for Floating Garbage on Water Surfaces Based on Unmanned Surface Vessels | 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 Multi-Target Tracking Algorithm for Floating Garbage on Water Surfaces Based on Unmanned Surface Vessels Heping Yuan, Zhou Bo, Fengrong Guo, Bijin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7493863/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 To address the challenge of high-precision real-time detection and tracking of small floating debris from an unmanned surface vehicle (USV) perspective under limited computational resources, this paper first constructs a multi-object tracking model, YOLOv5s-B, by integrating the YOLOv5 detector with the Byte data association algorithm, and then proposes an enhanced framework, YOLO-EENB. The proposed framework employs a lightweight EfficientNetV2 backbone to substantially reduce model complexity while preserving feature extraction capability; incorporates an Efficient Channel Attention (ECA) module to enhance feature representation with minimal computational overhead; and introduces a distance-aware loss function based on the Normalized Wasserstein Distance (NWD) to optimize small-object bounding-box regression accuracy.To evaluate the performance of the proposed method, comparative experiments were conducted on a custom floating debris dataset, benchmarking YOLO-EENB against the baseline YOLOv5s-B model. Experimental results demonstrate that YOLO-EENB achieves improvements of 7.5%, 10.1%, 12.2%, and 11.8% in IDF1, IDR, Recall, and MOTA metrics, respectively, while reducing FN, FP, and ID switches by 28.8%, 20%, and 7.1%. Moreover, the proposed model attains approximately 24.5% higher FPS, indicating superior real-time performance and computational efficiency. Finally, YOLO-EENB is deployed on a USV platform, achieving stable, continuous tracking without ID switches or target loss in practical scenarios. The proposed solution offers an efficient and feasible approach for intelligent water-surface cleaning systems operating under resource constraints. Floating debris detection Multi-object tracking YOLOv5 Byte data association EfficientNetV2 Normalized Wasserstein Distance Unmanned surface vehicle 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. 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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-7493863","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528366639,"identity":"7383ac49-ee11-4cf6-b253-bccf373e3a5e","order_by":0,"name":"Heping Yuan","email":"","orcid":"","institution":"Xiamen University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Heping","middleName":"","lastName":"Yuan","suffix":""},{"id":528366641,"identity":"8276e8e5-e18a-4657-9716-a8fd3a99f76d","order_by":1,"name":"Zhou Bo","email":"","orcid":"","institution":"Xiamen University of 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