Open Access

ARTICLE

Research of Insect Recognition Based on Improved YOLOv5

Zhong Yuan¹, Wei Fang^1,2,*, Yongming Zhao^3,*, Victor S. Sheng⁴

1 School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
3 China Meteorological Administration Training Center, Beijing, 100081, China 4Department of Computer, Texas Tech University, Lubbock, TX 79409, USA

* Corresponding Authors: Wei Fang. Email: ; Yongming Zhao. Email:

Journal on Artificial Intelligence 2021, 3(4), 145-152. https://doi.org/10.32604/jai.2021.026902

Received 06 January 2022; Accepted 21 January 2022; Issue published 07 February 2022

Abstract

Insects play an important role in the natural ecology, it is of great significance for ecology to research on insects. Nowadays, the invasion of alien species has brought serious troubles and a lot of losses to local life. However, there is still much room for improvement in the accuracy of insect recognition to effectively prevent the invasion of alien species. As the latest target detection algorithm, YOLOv5 has been used in various scene detection tasks, because of its powerful recognition capabilities and extremely high accuracy. As the problem of imbalance of feature maps at different scales will affect the accuracy of recognition, we propose that adding an attention mechanism based on YOLOv5. The channel attention module and the spatial attention module are added to highlight the important information in the feature map and weaken the secondary information, enhancing the recognition ability of the network. Through training on self-made insect data sets, experimental results show that the mAP@0.5 value reaches 92.5% and the F1 score reaches 0.91. Compared with YOLOv5, the map has increased by 1.7%, and the F1 score has increased by 0.02, proving the effectiveness of insect recognition based on improved YOLOv5. In conclusion, we provide effective technical support for insect identification, especially for pest identification.

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Keywords

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