Open Access
ARTICLE
Research of Insect Recognition Based on Improved YOLOv5
Zhong Yuan1, Wei Fang1,2,*, Yongming Zhao3,*, 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.
Keywords
Cite This Article
Z. Yuan, W. Fang, Y. Zhao and V. S. Sheng, "Research of insect recognition based on improved yolov5,"
Journal on Artificial Intelligence, vol. 3, no.4, pp. 145–152, 2021. https://doi.org/10.32604/jai.2021.026902