Deep learning based automatic multi-class wild pest monitoring approach using hybrid global and local activated features

Liu, L., Xie, C.J., Wang, R.J. et al. (5 more authors) (2021) Deep learning based automatic multi-class wild pest monitoring approach using hybrid global and local activated features. IEEE Transactions on Industrial Informatics, 17 (11). pp. 7589-7598. ISSN 1551-3203

Abstract

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Copyright, Publisher and Additional Information: © 2020 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: Convolutional Neural Network; Pest Monitoring; Global Activated Feature Pyramid Network; Local Activated Region Proposal Network
Dates:
  • Accepted: 3 May 2020
  • Published (online): 18 May 2020
  • Published: November 2021
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: 05 May 2020 10:08
Last Modified: 23 Nov 2021 11:08
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: https://doi.org/10.1109/TII.2020.2995208

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