RT-DETR-Based Object Detection and Parameter Extraction for Wireless Signal Spectrograms

RT-DETR-Based Object Detection and Parameter Extraction for Wireless Signal Spectrograms | 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 Article RT-DETR-Based Object Detection and Parameter Extraction for Wireless Signal Spectrograms Zhibo Shi, Rui Zhu, Lulu Liu, Yaru Li, Hongyan Li, Juan Tian, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9297580/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Traditional modulation recognition struggles with multi-signal coexistence and low signal-to-noise ratios. This paper proposes an integrated framework for wideband signal detection, recognition, and parameter extraction using object detection technology. By converting signals into spectrograms via Short-Time Fourier Transform, the framework employs RT-DETR as the backbone for feature extraction. Key modules, including Transformer-based Intra-scale Feature Interaction and CNN-based Cross-scale Feature Fusion, are introduced to overcome local convolution limitations, enabling precise end-to-end localization and classification. Furthermore, the system directly extracts physical parameters, such as center frequency and bandwidth, from predicted bounding box geometries. Using a dataset of nine modulation types, experiments across an SNR range of -25 dB to 25 dB demonstrate that RT-DETR outperforms YOLOv8, YOLOv10, and YOLOv11 in noise resistance and feature representation. RT-DETR significantly reduces missed detections and false alarms in multi-class tasks, achieving superior average precision, recall, and Normalized Root Mean Square Error for parameter extraction. This research offers an efficient approach for intelligent spectrum sensing and non-cooperative reconnaissance in complex environments. Physical sciences/Engineering Physical sciences/Mathematics and computing Automatic Modulation Recognition Object Detection RT-DETR Spectrogram Parameter Extraction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 12 May, 2026 Reviews received at journal 02 May, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 10 Apr, 2026 Editor invited by journal 09 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 01 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. 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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-9297580","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":621567506,"identity":"726715a2-9d6f-40e3-9be0-e666e3c26b51","order_by":0,"name":"Zhibo Shi","email":"","orcid":"","institution":"Xijing University","correspondingAuthor":false,"prefix":"","firstName":"Zhibo","middleName":"","lastName":"Shi","suffix":""},{"id":621567511,"identity":"939325ec-c3be-4535-8e00-6cb51a06cf13","order_by":1,"name":"Rui 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