{"paper_id":"1527c30a-82cd-4743-8ace-d637ee4e3efe","body_text":"A remote sensing target detection model based on lightweight feature enhancement and feature refinement extraction | 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 A remote sensing target detection model based on lightweight feature enhancement and feature refinement extraction Dongen Guo, Zhuoke Zhou, Fengshuo Guo, Chaoxin Jia, xiaohong Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3629661/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jan, 2024 Read the published version in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing → Version 1 posted You are reading this latest preprint version Abstract Remote sensing image (RSI) target detection methods based on traditional multi scale feature fusion (MSFF) have achieved great success. However, the traditional MSFF method significantly increases the computational cost during model training and inference, and the simple fusion operation may lead to the semantic confusion of the feature map, which cannot realize the refined extraction of features by the model. In order to reduce the computational effort associated with the MSFF operation and to enable the features in the feature map to present an accurate, fine-grained distribution, we propose a single-stage detection model(RS-YOLO). Our main additions to RS-YOLO are a computationally smaller and faster QS-E-ELEN (Quick and Small E-ELEN) module and a feature refinement extraction (FRE) module. In the QS-E-ELEN module We utilize QSBlock,jump-join, and convolution operations to fuse features on different scales and reduce the computational effort of the model by exploiting the similarity of the RSI feature map channels. In order for the model to better utilize the enhanced features, FRE makes the feature mapping of the target to be detected in the RSI accurate and refined. By conducting experiments on the popular NWPU-VHR- 10 and SSDD datasets, we derive results that show that RS-YOLO outperforms most mainstream models in terms of the trade-off between accuracy and speed. Specifically, in terms of accuracy, it improves 1.6% and 1.7% compared to the current state-of-the-art models, respectively. At the same time, RS-YOLO reduces the number of parameters and computational effort. Feature fusion Object detection Feature Refinement Remote sensing image Full Text Cite Share Download PDF Status: Published Journal Publication published 01 Jan, 2024 Read the published version in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing → 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-3629661\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":306323729,\"identity\":\"eb0cd1eb-2aaa-4fa9-ba37-9187d5b4362e\",\"order_by\":0,\"name\":\"Dongen 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