A large-scale container dataset and a baseline method for container hole localization

Diao, Y, Tang, X, Wang, H orcid.org/0000-0002-2281-5679 et al. (4 more authors) (2022) A large-scale container dataset and a baseline method for container hole localization. Journal of Real-Time Image Processing, 19 (3). pp. 577-589. ISSN 1861-8200

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Authors/Creators:
Copyright, Publisher and Additional Information: © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11554-022-01199-y. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Container keyhole localization; Salient object segmentation; Deep learning; Container dataset
Dates:
  • Accepted: 6 January 2022
  • Published (online): 2 March 2022
  • Published: June 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 01 Apr 2022 15:11
Last Modified: 27 Jun 2022 09:16
Status: Published
Publisher: Springer
Identification Number: https://doi.org/10.1007/s11554-022-01199-y
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