Research on recognition and localization method of maize weeding robot based on improved YOLOv5

Research on recognition and localization method of maize weeding robot based on improved YOLOv5 | 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 Research on recognition and localization method of maize weeding robot based on improved YOLOv5 Lijun Zhao, Yunfan Jia, Wenke Yin, Zihuan Li, Chuandong Liu, Hang Luo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4800448/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 In response to the challenge posed by low recognition accuracy in rugged terrains with diverse topography as well as feature recognition agricultural settings. This paper presents an optimized version of the YOLOv5 algorithm alongside the development of a specialized laser weeding experimental platform designed for precise identification of corn seedlings and weeds. The enhanced YOLOv5 algorithm integrates the effective channel attention (CBAM) mechanism while incorporating the DeepSort tracking algorithm to reduce parameter count for seamless mobile deployment. Ablation test validate our model's achievement of 96.2% accuracy along with superior mAP values compared to standard YOLOv5 by margins of 3.1% and 0.7%, respectively. Additionally, three distinct datasets capturing varied scenarios were curated; their amalgamation resulted in an impressive recognition rate reaching up to 96.13%. Through comparative assessments against YOLOv8, our model demonstrates lightweight performance improvements including a notable enhancement of 2.1% in recognition rate coupled with a marginal increase of 0.2% in mAP value, thus ensuring heightened precisionand robustness during dynamic object detection within intricate backgrounds. Biological sciences/Biological techniques/Software Biological sciences/Biological techniques Biological sciences/Biotechnology Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Full Text Additional Declarations No competing interests reported. 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-4800448","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":346877798,"identity":"4d8fb0f3-935f-4515-bab7-d87fc9b95456","order_by":0,"name":"Lijun Zhao","email":"","orcid":"","institution":"College of Intelligent and Manufacturing Engineering, Chongqing University of Arts and Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Zhao","suffix":""},{"id":346877799,"identity":"f48ee5c3-c184-4267-b80f-6c36d67d072d","order_by":1,"name":"Yunfan Jia","email":"","orcid":"","institution":"College of 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This paper presents an optimized version of the YOLOv5 algorithm alongside the development of a specialized laser weeding experimental platform designed for precise identification of corn seedlings and weeds. The enhanced YOLOv5 algorithm integrates the effective channel attention (CBAM) mechanism while incorporating the DeepSort tracking algorithm to reduce parameter count for seamless mobile deployment. Ablation test validate our model's achievement of 96.2% accuracy along with superior mAP values compared to standard YOLOv5 by margins of 3.1% and 0.7%, respectively. Additionally, three distinct datasets capturing varied scenarios were curated; their amalgamation resulted in an impressive recognition rate reaching up to 96.13%. 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