An Image Recognition Algorithm for Fine-grained High-Frequency Workpieces Based on a Multi- branch Network Architecture

An Image Recognition Algorithm for Fine-grained High-Frequency Workpieces Based on a Multi- branch Network Architecture | 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 An Image Recognition Algorithm for Fine-grained High-Frequency Workpieces Based on a Multi- branch Network Architecture Jiaqi Deng, Chenglong Sun, Jiajie Lin, Yichen He, Yipeng Yin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7949835/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract To address the low recognition accuracy of high-frequency workpiece images caused by complex intra-class textures and minor inter-class differences in top-surface features, we propose a Multi-Branch EfficientNet (MBEN) algorithm for recognizing fine-grained high-frequency workpieces. First, a weakly supervised region detection module is used to obtain discriminative regional images of the workpieces, which are then combined with global images to construct a multi-branch network that enhances the model’s multi-scale representational capacity. Next, by incorporating a weight-adjustment mechanism, we implement joint supervision using an adaptive cross-entropy loss and an adversarial center loss to guide the model toward intra-class compactness and inter-class separability of workpiece features. Finally, a branch fusion module is employed to augment the network’s attention to both global and local information, yielding improved fine-grained recognition performance. Experimental results demonstrate that the proposed algorithm effectively discriminates fine-grained high-frequency workpieces and outperforms existing models and methods in recognition accuracy, achieving an accuracy of 98.75%. Physical sciences/Engineering Physical sciences/Mathematics and computing high-frequency workpieces fine-grained image recognition multi-branch network region detection loss function Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviews received at journal 27 Nov, 2025 Reviews received at journal 19 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers invited by journal 12 Nov, 2025 Editor assigned by journal 12 Nov, 2025 Editor invited by journal 04 Nov, 2025 Submission checks completed at journal 02 Nov, 2025 First submitted to journal 02 Nov, 2025 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-7949835","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":549293040,"identity":"3190bd13-dfff-420e-a641-01e1ee12c8b7","order_by":0,"name":"Jiaqi 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