摘要
由于田间昆虫环境的复杂性、昆虫类别间样本数量的不均衡性,现有田间昆虫自动识别和分类方法存在误识率高、效率低等缺点。本文基于轻量化深度学习模型提出了新的田间昆虫自动识别和分类算法。首先,对田间昆虫图像进行预处理,将其输入到轻量化算法中进行特征提取,通过多尺度特征融合输出不同尺寸的预测网络。然后,引入联合交并比进行田间昆虫自动识别和分类。最后,与其他算法进行了仿真对比实验,结果表明,本文算法的田间昆虫自动识别和分类正确率高、用时少、鲁棒性强,有效解决了昆虫堆积、背景干扰等问题,可实时、在线识别田间昆虫。
Due to the complexity of the insect environment in the field and the imbalance in the number of samples among insect categories,the existing automatic identification and classification methods for field insects have high misidentification rates and low efficiency.In this paper,a new field insect automatic identification and classification algorithm is developed based on a lightweight deep learning model.First,preprocessing applied to the picture,then those images were input to the lightweight algorithm for feature extraction,and multi-scale feature fusion were adopted to output prediction networks of different sizes;then introduce joint cross-comparison for automatic identification and classification of field insects,and finally compare with the reference The algorithm has been simulated and compared.The results show that the field insect automatic identification and classification of the algorithm in this paper has high accuracy,less time,and strong robustness.It effectively solves the problems of insect accumulation and background interference and can identify field insects in real-time and online.
作者
袁哲明
袁鸿杰
言雨璇
李钎
刘双清
谭泗桥
YUAN Zhe-ming;YU AN Hong-jie;YAN Yu-xuan;LI Qian;LIU Shuang-qing;TAN Si-qiao(Hunan Engineering&Technology Research Center for Agricultural Big Data Analysis&Decision-makingHunan Agricultural University,Changsha 410128,China;Hunan Engineer Research Center for Information Technology in Agriculture,Hunan Agricultural University,Changsha 410128,China;Business School,Hunan Agricultural University,Changsha 410128,China;College of Plant Protection,Hunan Agricultural University,Changsha 410128,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2021年第3期1131-1139,共9页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(31772157)
中央引导地方科技发展专项资金项目(2019XF5015)
湖南省教育厅科学研究项目(17C0757)。
关键词
模式识别
深度学习
目标检测
昆虫分类
轻量化算法
图像预处理
pattern recognition
deep learning
object detection
insect classification
lightweight algorithm
image preprocessing
