Courier Manifest-OD: Large-scale Express Waybill Dataset for Target Detection in the Logistics Industry

Courier Manifest-OD: Large-scale Express Waybill Dataset for Target Detection in the Logistics Industry | 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 Courier Manifest-OD: Large-scale Express Waybill Dataset for Target Detection in the Logistics Industry Wenhong Wu, Wenlong Li, Xiaoni Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7001811/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The precise recognition of express waybills presents new challenges for the improvement and optimization of automated sorting equipment in the logistics industry. However, datasets for Chinese express waybills are almost non-existent, with most known datasets being in English and having various deficiencies in terms of quantity, scenario categories, and waybill specifications. This paper, in collaboration with Chinese logistics companies, proposes and constructs a large-scale express waybill dataset named CourierManifest-OD (CourierManifest-Object Detection Datasets). The dataset currently includes 5,000 express waybill images collected from four logistics companies, covering three common target categories and fourteen types of waybill specifications. It encompasses a variety of real-world scenarios, including complex conditions such as varying lighting, folds, damages, and stains. We applied several state-of-the-art deep learning detection methods, including EfficientDet, SSD, Faster-RCNN, and YOLO11, to detect targets in the express waybill dataset, exploring more efficient sorting strategies. The characteristics of CourierManifest-OD include a large scale of Chinese express waybill images, comprehensive coverage of object types, various waybill specifications, and diverse real-world scenarios. In the future, the CourierManifest-OD dataset will be made open source, hoping to provide strong support for research and applications in automated sorting in the logistics industry, and to promote technological innovation and practical implementation in this field. Health sciences/Diseases Health sciences/Medical research Automation Computer Vision Image Recognition Logistics Management Target Detection Full Text Additional Declarations No competing interests reported. Supplementary Files ResearchData.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Feb, 2026 Reviews received at journal 02 Jan, 2026 Reviewers agreed at journal 26 Dec, 2025 Reviews received at journal 03 Dec, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 05 Nov, 2025 Editor invited by journal 03 Nov, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 30 Oct, 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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