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Locust recognition and detection via aggregate channel features
conference contribution
posted on 2019-04-24, 15:17 authored by Dewei Yi, Jinya Su, Wen-Hua ChenLocust 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 TechnologiesCitation
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) NetworkVersion
- 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
2019-01-17Publication date
2019Notes
This is a conference paper.Publisher version
Language
- en