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Locust recognition and detection via aggregate channel features

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conference contribution
posted on 24.04.2019 by Dewei Yi, Jinya Su, Wen-Hua Chen
Locust plagues are very harmful for food security, quality and quantity of agricultural products. With this consideration, precise locust detection is significant for preventing locust plagues. To achieve this task, aggregate channel feature (ACF) object detector with parameters optimization is applied to detect locusts. Experiment results show that ACF object detector with optimized parameters can achieve 0.39 for average precision and 0.86 for log-average miss rate. Moreover, ACF is a non-deep method using a simple model to detect objects. That is, the proposed method is promising to be embedded in a real-time locust detection system.

Funding

This work was supported by the U.K. Science and Technology Facilities Council under Grant ST/N006852/1, ST/N006712/1, and ST/N006836/1.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

2nd UK-RAS ROBOTICS AND AUTONOMOUS SYSTEMS CONFERENCE, Loughborough, 2019 Embedded Inteligence: Enabling & Supporting RAS Technologies

Citation

YI, D., SU, J. and CHEN, W-H., 2019. Locust recognition and detection via aggregate channel features. Presented at the 2nd UK Robotics and Autonomous Systems Conference (UK-RAS 2019), Loughborough, UK, 24 January 2019, pp.112-115.

Publisher

EPSRC UK-Robotics and Autonomous Systems (UK-RAS) Network

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

17/01/2019

Publication date

2019

Notes

This is a conference paper.

Language

en

Location

Loughborough, Leicester, UK.

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