Improved varifocal net: a deep learning approach for rice pest detection

Shuaifeng Li; Heng Wang; Jie Liu; Xiaoyu Huang; Xiaoling Chen

doi:10.1117/12.2640722

13 October 2022 Improved varifocal net: a deep learning approach for rice pest detection

Shuaifeng Li, Heng Wang, Jie Liu, Xiaoyu Huang, Xiaoling Chen

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228723 (2022) https://doi.org/10.1117/12.2640722
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Rice is one of the most important crops in the world and the most important food crop in most areas. It is very susceptible to pests during the growing process, which affects the yield and quality of the rice. Accurately assessing the types of rice pests is the basis for pest eradication. A method for detecting rice pests based on improved Varifocal Net is proposed. A self-attention mechanism is introduced to improve and refine feature maps and a group normalization method is added. The experimental results show that the method can achieve accurate detection of 9 rice pests such as rice leaf roller and rice borer, with a mean average precision of 96.3%, which is 1.9% higher than the original Varifocal Net.

Citation Download Citation

Shuaifeng Li, Heng Wang, Jie Liu, Xiaoyu Huang, and Xiaoling Chen "Improved varifocal net: a deep learning approach for rice pest detection", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228723 (13 October 2022); https://doi.org/10.1117/12.2640722

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KEYWORDS

Feature extraction

Data modeling

Target detection

Visualization

Agriculture

Eye models

Lithium

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