Real-time Retail Planogram Compliance Application Using Computer Vision and Virtual Shelves

Real-time Retail Planogram Compliance Application Using Computer Vision and Virtual Shelves | 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 Real-time Retail Planogram Compliance Application Using Computer Vision and Virtual Shelves Tsung-Yin Ou, Andrés Ponce, Cody Lee, Areoll Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7463367/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 3 You are reading this latest preprint version Abstract This study addresses the challenge of planogram compliance in convenience stores by proposing a scalable, automated shelf monitoring system deployed across over 7,000 7-Eleven stores in Taiwan. Traditional manual audits are labor-intensive, error-prone, and costly, creating a growing need for reliable, automated solutions. To address this challenge, the proposed system integrates computer vision and deep learning techniques into a unified pipeline capable of detecting shelves, recognizing products, and comparing shelf layouts against digital planograms through a customized alignment algorithm. The system further incorporates multi-image stitching to overcome spatial constraints and construct virtual shelves that closely replicate real-world environments, improving adaptability and accuracy. Three large-scale datasets were developed to support model training and validation: 15,232 images for shelf detection, 99,135 images for product detection, and 471 product categories averaging 210 images each for classification. Automated labeling and clustering processes were introduced to substantially reduce manual annotation time. Experimental results demonstrate that the YOLOv8-based detection models achieve exceptional precision and recall across all stages. For shelf detection, the model achieved 99.23% precision, 98.93% recall, and 99.41% mAP@50, while product detection reached 94.61% precision, 93.02% recall, and 95.7% mAP@50—both surpassing transformer-based alternatives such as Deformable DETR. ResNet101 and FAN-based Transformer models achieved 99.86% accuracy on real-world retail datasets, indicating strong model stability. In the few-shot experiments, the FAN-based model showed strong adaptability and generalization, maintaining high accuracy with only five samples per class and achieving 98.39% Top-1 and 99.48% Top-5 accuracy on unseen products, demonstrating excellent transfer learning and real-time recognition capability. The system offers high accuracy, scalability, and real-time efficiency, making it a strong alternative to manual audits and a driver of smart retail innovation. Physical sciences/Engineering Physical sciences/Mathematics and computing Planogram Compliance Computer Vision Deep Learning Virtual Shelves Automated Labeling and Clustering Processes Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 05 Nov, 2025 Submission checks completed at journal 05 Nov, 2025 First submitted to journal 04 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. We do this by developing innovative software and high quality services for the global research community. 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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-7463367","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":540479462,"identity":"57ade888-5741-4582-8b66-38619cc48d4a","order_by":0,"name":"Tsung-Yin 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Shelves","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Planogram Compliance, Computer Vision, Deep Learning, Virtual Shelves, Automated Labeling and Clustering Processes","lastPublishedDoi":"10.21203/rs.3.rs-7463367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7463367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study addresses the challenge of planogram compliance in convenience stores by proposing a scalable, automated shelf monitoring system deployed across over 7,000 7-Eleven stores in Taiwan. Traditional manual audits are labor-intensive, error-prone, and costly, creating a growing need for reliable, automated solutions. To address this challenge, the proposed system integrates computer vision and deep learning techniques into a unified pipeline capable of detecting shelves, recognizing products, and comparing shelf layouts against digital planograms through a customized alignment algorithm. The system further incorporates multi-image stitching to overcome spatial constraints and construct virtual shelves that closely replicate real-world environments, improving adaptability and accuracy. Three large-scale datasets were developed to support model training and validation: 15,232 images for shelf detection, 99,135 images for product detection, and 471 product categories averaging 210 images each for classification. Automated labeling and clustering processes were introduced to substantially reduce manual annotation time.\u003c/p\u003e\u003cp\u003eExperimental results demonstrate that the YOLOv8-based detection models achieve exceptional precision and recall across all stages. For shelf detection, the model achieved 99.23% precision, 98.93% recall, and 99.41% mAP@50, while product detection reached 94.61% precision, 93.02% recall, and 95.7% mAP@50\u0026mdash;both surpassing transformer-based alternatives such as Deformable DETR. ResNet101 and FAN-based Transformer models achieved 99.86% accuracy on real-world retail datasets, indicating strong model stability. In the few-shot experiments, the FAN-based model showed strong adaptability and generalization, maintaining high accuracy with only five samples per class and achieving 98.39% Top-1 and 99.48% Top-5 accuracy on unseen products, demonstrating excellent transfer learning and real-time recognition capability. The system offers high accuracy, scalability, and real-time efficiency, making it a strong alternative to manual audits and a driver of smart retail innovation.\u003c/p\u003e","manuscriptTitle":"Real-time Retail Planogram Compliance Application Using Computer Vision and Virtual Shelves","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 05:30:02","doi":"10.21203/rs.3.rs-7463367/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-11-05T14:49:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-05T10:23:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-04T11:45:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"672ae730-4a35-4cf8-babc-2282791c0eb2","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57494398,"name":"Physical sciences/Engineering"},{"id":57494399,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-22T16:00:13+00:00","versionOfRecord":{"articleIdentity":"rs-7463367","link":"https://doi.org/10.1038/s41598-025-27773-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-16 15:57:22","publishedOnDateReadable":"December 16th, 2025"},"versionCreatedAt":"2025-11-06 05:30:02","video":"","vorDoi":"10.1038/s41598-025-27773-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27773-5","workflowStages":[]},"version":"v1","identity":"rs-7463367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7463367","identity":"rs-7463367","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}