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Probabilistic faster R-CNN with stochastic region proposing: Towards object detection and recognition in remote sensing imagery

journal contribution
posted on 15.07.2021, 09:13 by Dewei Yi, Jinya Su, Wen-Hua Chen
Object detection is one of the most important tasks involved in intelligent agriculture systems, especially in pest detection. This paper focuses on a most devastated agricultural disaster: grasshopper plagues. Grasshopper detection and monitoring is of paramount importance in preventing grasshopper plagues. This paper proposes a probabilistic faster R-CNN algorithm with stochastic region proposing, where a probabilistic region proposal network, an image classification network, and an object detection network are integrated to detect and locate grasshoppers. More specifically, in the proposed framework, the probabilistic region proposal network considers attributes (e.g. size, shape) of region proposals and the image classification network identifies the existence of grasshoppers while the object detection network scores recognition confidence for a region proposal. By integrating these three networks, the uncertainty can be passed from end to end, and the final confidence is obtained for each region proposal can be explicitly quantified. To enhance algorithm robustness, a stochastic region proposing algorithm is developed to screen region proposals rather than using a predetermined threshold. The proposed algorithm is validated by recently collected grasshopper datasets. The experimental results demonstrate that the proposed algorithm not only outperforms competing algorithms in terms of average precision (0.91), average missed rate (0.36), and maximum F1-score (0.9263), but also reduces the false positive rate of recognising the existence of grasshoppers in an open field.

Funding

Enabling wide area persistent remote sensing for agriculture applications by developing and coordinating multiple heterogeneous platforms

Science and Technology Facilities Council

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Integrating advanced earth observation and environmental information for sustainable management of crop pests and diseases

Science and Technology Facilities Council

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Neurocomputing

Volume

459

Pages

290 - 301

Publisher

Elsevier BV

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Neurocomputing and the definitive published version is available at https://doi.org/10.1016/j.neucom.2021.06.072

Acceptance date

25/06/2021

Publication date

2021-06-30

Copyright date

2021

ISSN

0925-2312

Language

en

Depositor

Prof Wen-Hua Chen Deposit date: 14 July 2021