@Article{jai.2021.026902,
AUTHOR = {Zhong Yuan, Wei Fang, Yongming Zhao, Victor S. Sheng},
TITLE = {Research of Insect Recognition Based on Improved YOLOv5},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {3},
YEAR = {2021},
NUMBER = {4},
PAGES = {145--152},
URL = {http://www.techscience.com/jai/v3n4/46711},
ISSN = {2579-003X},
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.},
DOI = {10.32604/jai.2021.026902}
}