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Research of insect Recognition Based on Improved YOLOv5

Journal on Artificial Intelligence 2021年第4期3卷 145-152页

作者： Zhong Yuan Wei Fang Yongming Zhao Victor S.Sheng School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing100081China China Meteorological Administration Training Center Beijing100081China Department of Computer Texas Tech UniversityLubbockTX 79409USA

insects play an important role in the natural ecology,it is of great significance for ecology to research on ***,the invasion of alien species has brought serious troubles and a lot of losses to local ***,there is still much room for improvement in the accuracy of insect recognition to effectively prevent the invasion of alien *** the latest target detection algorithm,YOLOv5 has been used in various scene detection tasks,because of its powerful recognition capabilities and extremely high *** 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 *** 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 *** training on self-made insect data sets,experimental results show that the mAP@0.5 value reaches 92.5%and the F1 score reaches *** 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 *** conclusion,we provide effective technical support for insect identification,especially for pest identification.

关键词： YOLOv5 attention mechanism insect identification

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