Deep Learning-Based Piglet Tracking Algorithm for Automated Crushing Detection on Pig Farms

Deep Learning-Based Piglet Tracking Algorithm for Automated Crushing Detection on Pig Farms | 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 Deep Learning-Based Piglet Tracking Algorithm for Automated Crushing Detection on Pig Farms Taeyong Yun, Jinhyeon Yun, Woongsup Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9144632/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 modern pig farming, piglets and sows are housed together in farrowing pens during the lactation period. However, piglets face the risk of being trapped and suffocated by the sow, a major cause of piglet mortality that significantly impacts farm productivity. To address this issue, various methods have been developed to detect piglet crushing using either acoustic or image data. However, these approaches often fail when the piglet is completely obstructed by the sow, rendering it hidden to both audio and image detection. In this paper, we propose a two-stage approach, consisting of a Hidden Trapping Prediction Algorithm (HPA) and a Crushing Decision Algorithm (CDA). The HPA tracks the total number of piglets, monitors those that are unseen, and detects changes in their count, allowing us to predict which unseen piglets are likely to be trapped. The CDA uses a YOLO model to track newly detected piglets, estimating their movements and identifying objects that remain stationary for a certain period as likely to be crushed. We also developed the Trapping Prediction Algorithm (TPA), which combines an image-based trapping detection model with the HPA. This model assesses the number of objects per frame and analyzes the movement of new objects. To evaluate our scheme, we collected the video footage from five litters housed in loose farrowing pens, capturing both trapping and crushing events. Our performance evaluation confirmed that the proposed scheme efficiently tracks piglets and predicts hidden trapping events and crush events with a mean absolute error (MAE) of 1 and 0.6 respectively. Furthermore, our scheme achieved high accuracy in detecting piglet trapping events, with a R2 value of 0.49, surpassing existing image-based models. Biological sciences/Computational biology and bioinformatics 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-9144632","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":613691779,"identity":"1cf4872b-ad02-487b-bff3-42b9d1e3a0c7","order_by":0,"name":"Taeyong Yun","email":"","orcid":"","institution":"Yonsei University","correspondingAuthor":false,"prefix":"","firstName":"Taeyong","middleName":"","lastName":"Yun","suffix":""},{"id":613691780,"identity":"c53e51fc-ad5c-46eb-a2b5-b727579558ac","order_by":1,"name":"Jinhyeon Yun","email":"","orcid":"","institution":"Chonnam National University","correspondingAuthor":false,"prefix":"","firstName":"Jinhyeon","middleName":"","lastName":"Yun","suffix":""},{"id":613691781,"identity":"17791c32-1a88-4779-96eb-46ce27b66f27","order_by":2,"name":"Woongsup Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie2RMWrDMBSGnxAoS0pWC6f0Ci4Gl1KXXqWi4CyNF0OnDA8M8pIDZPMVcoQEg7KYzllN1w4eCjXUhcr2kqFqyBaoPpD4Jd7HLxCAxXLWjPSq+0Sw273jCtWzq5MVOj44GxVvl6rqoy1ioHT7di/D+CpLEZoF+DdoUEo18y9lkQBlT/5cRolXbpEsFQTTze9KsH8OXI6FQDoO3LksxNoRCBcIoWN4WK84badMPr9utZLnFZLvIwqvWd/CKNEK7glS3RKYlIdSRS6Rs4RR5vPlayTWpcBiqhzfpPAsVbxp7+LJKK3q5iUUebarqvdFeL0yKB3ddzyyLhE23GwATB0DpNHKENs/By0Wi+Wf8gPfjVgIo8ve0gAAAABJRU5ErkJggg==","orcid":"","institution":"Yonsei University","correspondingAuthor":true,"prefix":"","firstName":"Woongsup","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2026-03-17 06:08:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9144632/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9144632/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107991417,"identity":"5ed2c1df-0474-49cf-a983-b679d1e3a216","added_by":"auto","created_at":"2026-04-28 10:11:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":769573,"visible":true,"origin":"","legend":"","description":"","filename":"AutomatedCrushingDetectiontoScientificReportsSubmit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9144632/v1_covered_25bad174-da18-4af4-9a76-57ddb346cef9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning-Based Piglet Tracking Algorithm for Automated Crushing Detection on Pig Farms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9144632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9144632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In modern pig farming, piglets and sows are housed together in farrowing pens during the lactation period. 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We also developed the Trapping Prediction Algorithm (TPA), which combines an image-based trapping detection model with the HPA. This model assesses the number of objects per frame and analyzes the movement of new objects. To evaluate our scheme, we collected the video footage from five litters housed in loose farrowing pens, capturing both trapping and crushing events. Our performance evaluation confirmed that the proposed scheme efficiently tracks piglets and predicts hidden trapping events and crush events with a mean absolute error (MAE) of 1 and 0.6 respectively. 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