Intelligent Counting of Coal-Mine Drill Pipes via an Oriented-Detection YOLO11-OD Framework

Intelligent Counting of Coal-Mine Drill Pipes via an Oriented-Detection YOLO11-OD Framework | 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 Intelligent Counting of Coal-Mine Drill Pipes via an Oriented-Detection YOLO11-OD Framework LIN Yipeng, Zhigang LIU, YOU Wuchao, YUAN Jianbo, LI Zhiwei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7543140/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 Accurate counting of drill pipes in underground coal mines is critical for evaluating drilling progress and ensuring operational safety. Manual counting is inefficient and prone to high error rates. Meanwhile, existing vision-based methods frequently lack robustness in challenging underground conditions, such as low illumination, high dust concentration, and frequent occlusions. We propose an improved YOLO11-based detection and counting framework, termed YOLO11-OD, featuring: (i) an EfficientNetV2 backbone with MBConv and Fused-MBConv blocks, enhanced by ECA and CBAM attention; (ii) oriented bounding boxes with Kullback–Leibler Divergence (KLD) loss for improved orientation-aware detection; (iii) anchor optimization via K-means clustering to capture elongated drill pipe geometries; and (iv) a multi-condition counting strategy integrating DeepSORT tracking with displacement, velocity, and angular features. Field experiments demonstrate that YOLO11-OD achieves a mean average precision ( [email protected] ) of 81.3% and reduces the counting error rate to 7.2%, showing a significant improvement over the baseline YOLO11 (which had a [email protected] of 71.5% and an error rate of 14.3%). These results highlight YOLO11-OD as an effective and robust solution for intelligent drill pipe monitoring in coal mine environments. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7543140","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":519105392,"identity":"a1bc83b5-4e57-4d50-84b5-08b76a24489e","order_by":0,"name":"LIN Yipeng","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"LIN","middleName":"","lastName":"Yipeng","suffix":""},{"id":519105393,"identity":"52ef4396-2b4d-4db5-a230-8b5287a498db","order_by":1,"name":"Zhigang 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Manual counting is inefficient and prone to high error rates. Meanwhile, existing vision-based methods frequently lack robustness in challenging underground conditions, such as low illumination, high dust concentration, and frequent occlusions. We propose an improved YOLO11-based detection and counting framework, termed YOLO11-OD, featuring: (i) an EfficientNetV2 backbone with MBConv and Fused-MBConv blocks, enhanced by ECA and CBAM attention; (ii) oriented bounding boxes with Kullback–Leibler Divergence (KLD) loss for improved orientation-aware detection; (iii) anchor optimization via K-means clustering to capture elongated drill pipe geometries; and (iv) a multi-condition counting strategy integrating DeepSORT tracking with displacement, velocity, and angular features. 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