PestNet : an end-to-end deep learning approach for large-scale multi-class pest detection and classification

Liu, L., Wang, R., Xie, C. et al. (4 more authors) (2019) PestNet : an end-to-end deep learning approach for large-scale multi-class pest detection and classification. IEEE Access, 7. pp. 45301-45312.

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Channel-spatial attention; convolutional neural network; multi-class pest detection; position-sensitive score map; region proposal network
Dates:
  • Accepted: 28 March 2019
  • Published (online): 9 April 2019
  • Published: 10 April 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 18 Sep 2019 15:23
Last Modified: 19 Sep 2019 23:28
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: https://doi.org/10.1109/access.2019.2909522

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