A Computer Vision-Based Detection Model for Wild Chinese Giant Salamanders in Complex Environments

A Computer Vision-Based Detection Model for Wild Chinese Giant Salamanders in Complex Environments | 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 A Computer Vision-Based Detection Model for Wild Chinese Giant Salamanders in Complex Environments junyi chen, xinyao Yang, Nanqing Sun, Didi Lu, Hang Gao, da Qiu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7517311/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 achieve rapid and accurate identification of wild Chinese giant salamanders in complex field environments, we propose an improved recognition model based on YOLO v11n. This model incorporates an Efficient Multi-scale Attention Module (EMA) into the Backbone layer, replaces the Complete Intersection over Union (CIoU) loss function with Wise-IoU (WIoU) loss, and introduces Lightweight Adaptive Extraction of Convolutions (LAE) into the Head layer. Ablation and comparative experiments demonstrate that the improved model achieves recall, precision, F1 score, and frame rate of 94.85%, 95.39%, 95.12%, and 77.20 f/s, respectively. The model occupies 11.56 MB of memory and performs 8.65×10⁹ floating-point operations. Compared to the baseline YOLO v11n, the recall, precision, F1 score, and frame rate are 5.70, 6.13, 5.92 percentage points higher and 27.1 fps faster, respectively. The proposed YOLO v11n-EWL model demonstrates significant improvements in stability, recognition speed, and accuracy. The improved model meets the real-time detection requirements for wild Chinese giant salamanders in natural habitats and can sustain long-term outdoor operation. Based on this, an all-weather image recognition and behavior detection system for wild salamanders was developed, successfully detecting over 20 instances of wild Chinese giant salamanders. This system provides a stable and reliable monitoring tool for advancing the conservation of wild salamanders and their habitats. Scientific community and society/Forestry Biological sciences/Computational biology and bioinformatics/Computational models Physical sciences/Engineering/Electrical and electronic engineering Chinese giant salamander object detection YOLO v11 Deep learning Full Text Additional Declarations There is NO Competing Interest. 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. 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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-7517311","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509739679,"identity":"54d14a59-26b3-40bb-bf92-d415e9097fc0","order_by":0,"name":"junyi 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