Research on Royal Jelly Impurity Detection Based on the CARCAW-YOLOv11 Algorithm

Research on Royal Jelly Impurity Detection Based on the CARCAW-YOLOv11 Algorithm | 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 Research on Royal Jelly Impurity Detection Based on the CARCAW-YOLOv11 Algorithm Yijun Guo, Kaixuan Li, Shihao Guan, Chao Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8656056/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Royal jelly is a highly nutritious and health-beneficial bee product. However, its production process often involves impurities such as beeswax and bee pupae, which compromises its quality and value. Traditional methods for detecting impurities in royal jelly primarily rely on manual experience, leading to inefficiency and subjectivity, which fails to satisfy the demand for rapid and accurate detection. To address this, this paper proposed the CARCAW-YOLOv11 (Content-Aware Reinforced Coordinate Attention Wise-YOLOv11) algorithm for the rapid detection of royal jelly impurities. Based on the YOLOv11 architecture, the proposed method incorporated the CARAFE feature enhancement module, the Coordinate Attention (CA) mechanism, and an optimized Wise-IoU loss function. Experimental results indicated that the CARAFE module effectively reduced information loss during upsampling, providing higher-quality features for subsequent detection. The CA mechanism enhanced the model's detection precision and recall, and the optimized Wise-IoU loss function improved the detection capability for small objects and reduced computational complexity. The CARCAW-YOLOv11 model achieved a precision of 91.7%, a recall of 80.5%, an [email protected] of 87.3%, and an F1-score of 0.79, with a detection speed of 140.2 FPS. Compared with models such as YOLOv8n, YOLOv9s, Faster R-CNN, and DETR, CARCAW-YOLOv11 model demonstrated a significant improvement in detection accuracy for tiny targets and robustness in the royal jelly impurity detection. This study provided a novel, fast, and accurate approach for the quality control of royal jelly. Royal jelly impurity detection YOLOv11 Small object detection Attention mechanism Full Text Additional Declarations No competing interests reported. Supplementary Files 121highlights.docx 121graphicalabstract.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 16 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 23 Jan, 2026 Submission checks completed at journal 23 Jan, 2026 First submitted to journal 21 Jan, 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-8656056","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582339646,"identity":"d20ed30c-7a17-4dd9-807c-cb275f34d666","order_by":0,"name":"Yijun Guo","email":"","orcid":"","institution":"Zhejiang A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Guo","suffix":""},{"id":582339647,"identity":"ccbe21a4-9f16-458e-8ffc-921e151a0675","order_by":1,"name":"Kaixuan Li","email":"","orcid":"","institution":"Zhejiang A\u0026F 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