Advancing HGV Detection with Limited Data: A Semantic Segmentation Framework Using SLiMe

Advancing HGV Detection with Limited Data: A Semantic Segmentation Framework Using SLiMe | 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 Advancing HGV Detection with Limited Data: A Semantic Segmentation Framework Using SLiMe Neda Darbandsari, Mo Saraee, Soheil Bazanjani, Taha Mansouri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7668213/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 Heavy Goods Vehicles (HGVs) segmentation is an essential enabler for intelligent transportation systems, vehicle accurate assessment, and traffic law enforcement. However, classical deep learning methods rely on dense pixel-level annotations, which are expensive and time-consuming to collect, particularly in cases involving specific HGV categories and rare traffic scenes. In this paper, we utilise a one-shot segmentation method, named SLiMe (Segment Like Me), which employs cross-attention and self-attention maps of pre-trained Stable Diffusion models to transfer the learned knowledge from a single-annotated reference to unseen target scenes. Compared to type-specific natural language object detectors such as YOLOv11 or generic segmenters such as SAM and CLIPSeg, SLiMe addresses class-specific segmentation with a single annotated example and eliminates any retraining or prompt generation effort.Our chosen approach demonstrates strong segmentation quality on buildings within various urban traffic scenes, outperforming baseline models in terms of mean IoU and pixel accuracy, particularly in challenging lighting conditions or occlusion scenarios. The results are encouraging for deploying SLiMe in transportation scenarios requiring limited annotation and fine-grained semantic understanding with a focus on real-time inference performance, highlighting the relevance for smart cities and autonomous vehicle perception systems. SLiMe semantic segmentation Stable Diffusion one-shot learning HGV detection traffic surveillance 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. 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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-7668213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531547320,"identity":"5828e35c-405b-4939-8885-34d9a8bc535a","order_by":0,"name":"Neda 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