Abnormal-Cut Tobacco Detection and Phenotypic Measurement Based on Improved YOLOv5s Rotating Frame

Abnormal-Cut Tobacco Detection and Phenotypic Measurement Based on Improved YOLOv5s Rotating Frame | 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 Abnormal-Cut Tobacco Detection and Phenotypic Measurement Based on Improved YOLOv5s Rotating Frame Jiakang LI, Donghui HU, Erqiang ZHANG, Jie ZHANG, Hui LI, Shihuan CHEN, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6884293/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Feb, 2026 Read the published version in Journal of Real-Time Image Processing → Version 1 posted 9 You are reading this latest preprint version Abstract To accurately locate and count abnormal-cut tobacco in complex tobacco-making environments such as interference and occlusion, this study proposes an improved YOLOv5s method for detecting and counting abnormal-cut tobacco rotating frames. Firstly, the C3-DEBlock is constructed by combining the efficient multi-scale attention ( EMA ) module, dynamic snake convolution, and C3 module in the backbone network to adaptively adjust the receptive field, to enhance the feature extraction ability; Secondly, the context-anchor bidirectional feature pyramid network CAA-BiFPN is introduced into the neck network to capture the long-distance context information and improve the multi-scale feature fusion ability. Finally, the Kullback-Leibler divergence between Gaussian distributions is used as the regression loss function, so that the parameter gradient can be dynamically adjusted according to the characteristics of the object, to perform the regression of the detection box more accurately. Experiments show that compared with the mainstream object detection models FasterR-CNN, YOLOv4-tiny, and YOLOv5s, mAP increases by 14.91, 25.21, and 2.61 percentage points respectively. Regression analysis is performed by measuring the length and width of abnormal-cut tobacco manually, and the determination coefficients were 0.98, 0.985, 0.99, 0.985, 0.995, and 0.98, respectively. This method accurately locates abnormal-cut tobacco by rotating frame technology, which significantly reduces the interference of the background area. It provides an online detection scheme for accurate counting of abnormal-cut tobacco and precise grading control and optimization of cutting quality, which helps promote the modernization process of tobacco processing. Cutting quality Anomaly detection Object detection YOLOv5s Rotational bounding box Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Feb, 2026 Read the published version in Journal of Real-Time Image Processing → Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviews received at journal 03 Oct, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 15 Jun, 2025 Editor assigned by journal 13 Jun, 2025 Submission checks completed at journal 13 Jun, 2025 First submitted to journal 12 Jun, 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. 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-6884293","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471635184,"identity":"24aea74b-e68a-4c1a-aaaa-c5c1c729b067","order_by":0,"name":"Jiakang LI","email":"","orcid":"","institution":"Key Laboratory of Tobacco Processing, Zhengzhou Tobacco Research Institute of CNTC","correspondingAuthor":false,"prefix":"","firstName":"Jiakang","middleName":"","lastName":"LI","suffix":""},{"id":471635185,"identity":"23610268-0dad-4e03-9651-1163a2760b43","order_by":1,"name":"Donghui HU","email":"","orcid":"","institution":"Key Laboratory of Tobacco Processing, Zhengzhou Tobacco Research Institute of CNTC","correspondingAuthor":false,"prefix":"","firstName":"Donghui","middleName":"","lastName":"HU","suffix":""},{"id":471635186,"identity":"2988bd1f-6869-4398-8cc8-6ee11a25daff","order_by":2,"name":"Erqiang ZHANG","email":"","orcid":"","institution":"Technology Center, China Tobacco Shaanxi Industrial Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Erqiang","middleName":"","lastName":"ZHANG","suffix":""},{"id":471635187,"identity":"9a2ea855-8f72-4ee4-9b69-6c7b7bc59c31","order_by":3,"name":"Jie ZHANG","email":"","orcid":"","institution":"College of Electrical and Information Engineering Zhengzhou University of Light Industry","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"ZHANG","suffix":""},{"id":471635188,"identity":"b0ad53b4-3e4d-463c-bc2c-1176d1bf7831","order_by":4,"name":"Hui LI","email":"","orcid":"","institution":"Key Laboratory of Tobacco Processing, Zhengzhou Tobacco Research Institute of CNTC","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"LI","suffix":""},{"id":471635189,"identity":"f6c9a89e-6ee0-4579-9ecb-af8f0da9bb4b","order_by":5,"name":"Shihuan CHEN","email":"","orcid":"","institution":"Technology Center, China Tobacco Fujian Industrial Co.,Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Shihuan","middleName":"","lastName":"CHEN","suffix":""},{"id":471635190,"identity":"814866a9-f69c-4ed3-a3fb-72c294237b4a","order_by":6,"name":"Jinsong DU","email":"","orcid":"","institution":"Key Laboratory of Tobacco Processing, Zhengzhou Tobacco Research Institute of CNTC","correspondingAuthor":false,"prefix":"","firstName":"Jinsong","middleName":"","lastName":"DU","suffix":""},{"id":471635191,"identity":"a15c6f60-a89c-4d5b-a8dc-14a7adfb508f","order_by":7,"name":"Dayong XU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYFCCBBBhw8PP3kCaljQZyZ4DpGk5bGNww4FIDQbHcwwfF/w6z8Nwg4Hxw8ccIrRI9rwxNp7Zd5uHcXYDs+TMbURo4ZfIMZPm7bnNwyxzgI2ZlxgtbBAt53jYJBKI1AK2hefHAR4eorVI9jwrNuZtSOaR4DnYTJxfDI4nb3zM88fO3v5488EPH4nRwsDAYcDA2AZiMDYQpR4I2B8wMPwhVvEoGAWjYBSMSAAAyh4yulK1dJ0AAAAASUVORK5CYII=","orcid":"","institution":"Key Laboratory of Tobacco Processing, Zhengzhou Tobacco Research Institute of CNTC","correspondingAuthor":true,"prefix":"","firstName":"Dayong","middleName":"","lastName":"XU","suffix":""}],"badges":[],"createdAt":"2025-06-13 03:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6884293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6884293/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11554-026-01860-w","type":"published","date":"2026-02-24T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103765752,"identity":"4a29e542-eb28-407f-a2cd-a14fa1596f16","added_by":"auto","created_at":"2026-03-02 16:08:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3742242,"visible":true,"origin":"","legend":"","description":"","filename":"AbnormalCutTobaccoDetectionandPhenotypicMeasurementBasedonImprovedYOLOv5sRotatingFrame.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6884293/v1_covered_508b1b57-1315-4bba-b996-967a8e233054.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Abnormal-Cut Tobacco Detection and Phenotypic Measurement Based on Improved YOLOv5s Rotating Frame","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-real-time-image-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rtip","sideBox":"Learn more about [Journal of Real-Time Image Processing](http://link.springer.com/journal/11554)","snPcode":"11554","submissionUrl":"https://submission.nature.com/new-submission/11554/3","title":"Journal of Real-Time Image Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cutting quality, Anomaly detection, Object detection, YOLOv5s, Rotational bounding box","lastPublishedDoi":"10.21203/rs.3.rs-6884293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6884293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To accurately locate and count abnormal-cut tobacco in complex tobacco-making environments such as interference and occlusion, this study proposes an improved YOLOv5s method for detecting and counting abnormal-cut tobacco rotating frames. Firstly, the C3-DEBlock is constructed by combining the efficient multi-scale attention ( EMA ) module, dynamic snake convolution, and C3 module in the backbone network to adaptively adjust the receptive field, to enhance the feature extraction ability; Secondly, the context-anchor bidirectional feature pyramid network CAA-BiFPN is introduced into the neck network to capture the long-distance context information and improve the multi-scale feature fusion ability. Finally, the Kullback-Leibler divergence between Gaussian distributions is used as the regression loss function, so that the parameter gradient can be dynamically adjusted according to the characteristics of the object, to perform the regression of the detection box more accurately. Experiments show that compared with the mainstream object detection models FasterR-CNN, YOLOv4-tiny, and YOLOv5s, mAP increases by 14.91, 25.21, and 2.61 percentage points respectively. Regression analysis is performed by measuring the length and width of abnormal-cut tobacco manually, and the determination coefficients were 0.98, 0.985, 0.99, 0.985, 0.995, and 0.98, respectively. This method accurately locates abnormal-cut tobacco by rotating frame technology, which significantly reduces the interference of the background area. It provides an online detection scheme for accurate counting of abnormal-cut tobacco and precise grading control and optimization of cutting quality, which helps promote the modernization process of tobacco processing.","manuscriptTitle":"Abnormal-Cut Tobacco Detection and Phenotypic Measurement Based on Improved YOLOv5s Rotating Frame","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 12:57:40","doi":"10.21203/rs.3.rs-6884293/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-07T12:04:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T03:13:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T19:48:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163191691739636321177817425708042528714","date":"2025-09-16T22:57:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311365750665836341939632268322915758273","date":"2025-09-08T13:58:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T01:54:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T06:55:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-13T06:55:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Real-Time Image Processing","date":"2025-06-13T03:31:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-real-time-image-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rtip","sideBox":"Learn more about [Journal of Real-Time Image Processing](http://link.springer.com/journal/11554)","snPcode":"11554","submissionUrl":"https://submission.nature.com/new-submission/11554/3","title":"Journal of Real-Time Image Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8b5bceec-26e6-4e0f-8b0c-50095d2cece3","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:05:34+00:00","versionOfRecord":{"articleIdentity":"rs-6884293","link":"https://doi.org/10.1007/s11554-026-01860-w","journal":{"identity":"journal-of-real-time-image-processing","isVorOnly":false,"title":"Journal of Real-Time Image Processing"},"publishedOn":"2026-02-24 15:57:27","publishedOnDateReadable":"February 24th, 2026"},"versionCreatedAt":"2025-06-18 12:57:40","video":"","vorDoi":"10.1007/s11554-026-01860-w","vorDoiUrl":"https://doi.org/10.1007/s11554-026-01860-w","workflowStages":[]},"version":"v1","identity":"rs-6884293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6884293","identity":"rs-6884293","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}