吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (3): 1131-1139.doi: 10.13229/j.cnki.jdxbgxb20200116

• 农业工程·仿生工程 • 上一篇    

基于深度学习的轻量化田间昆虫识别及分类模型

袁哲明1,2(),袁鸿杰1,2,言雨璇2,李钎2,3,刘双清4,谭泗桥1,2()   

  1. 1.湖南农业大学 湖南省农业大数据分析与决策工程技术研究中心，长沙 410128
    2.湖南农业大学 湖南省农村农业信息化工程技术研究中心，长沙 410128
    3.湖南农业大学 商学院，长沙 410128
    4.湖南农业大学 植物保护学院，长沙 410128
  • 收稿日期:2020-02-25 出版日期:2021-05-01 发布日期:2021-05-07
  • 通讯作者: 谭泗桥 E-mail:zhmyuan@sina.com;tsq@hunau.edu.cn
  • 作者简介:袁哲明（1971-），男，教授，博士生导师. 研究方向：生物信息学，复杂数据分析. E-mail:zhmyuan@sina.com
  • 基金资助:
    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)

Automatic recognition and classification of field insects based on lightweight deep learning model

Zhe-ming YUAN1,2(),Hong-jie YUAN1,2,Yu-xuan YAN2,Qian LI2,3,Shuang-qing LIU4,Si-qiao TAN1,2()   

  1. 1.Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making，Hunan Agricultural University，Changsha 410128，China
    2.Hunan Engineer Research Center for Information Technology in Agriculture，Hunan Agricultural University，Changsha 410128，China
    3.Business School，Hunan Agricultural University，Changsha 410128，China
    4.College of Plant Protection，Hunan Agricultural University，Changsha 410128，China
  • Received:2020-02-25 Online:2021-05-01 Published:2021-05-07
  • Contact: Si-qiao TAN E-mail:zhmyuan@sina.com;tsq@hunau.edu.cn

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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。

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

Abstract:

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.

Key words: pattern recognition, deep learning, object detection, insect classification, lightweight algorithm, image preprocessing

中图分类号: 

  • TP181

图1

轻量化昆虫检测与分类算法架构"

图2

深度可分离卷积原理"

表1

特征提取网络参数"

类型/步长卷积核尺寸输出尺寸
Conv/s23*3*3*32304*240*32
Conv_dw/s13*3*32304*240*32
Conv_dw/s11*1*32*64304*240*64
Conv_dw/s23*3*64152*120*64
Conv_dw/s11*1*64*128152*120*128
Conv_dw/s13*3*128152*120*128
Conv_dw/s11*1*128*128152*120*128
Conv_dw/s23*3*12876*60*128
Conv_dw/s11*1*128*25676*60*256
Conv_dw/s13*3*25676*60*256
Conv_dw/s11*1*256*25676*60*256
Conv_dw/s23*3*25638*30*256
Conv_dw/s11*1*256*51238*30*512
5×Conv_dw/s13*3*51238*30*512
Conv_dw/s11*1*512*51238*30*512
Conv_dw/s23*3*51219*15*512
Conv_dw/s11*1*512*102419*15*1024
Conv_dw/s13*3*102419*15*1024
Conv_dw/s11*1*1024*102419*15*1024

图3

IoU和GIoU的预测结果对比"

图4

部分昆虫训练集样本"

图5

各分类样本样本"

图6

训练损失函数曲线"

表2

本文目标检测模型对各测试集中各分类昆虫的预测效果"

昆虫类别AP/%
均值70.98
Chilo suppressalis70.69
Lepidoptera64.96
Hydrophilidae latreille87.38
Coleoptera71.02
Gryllotalpidae orientalis87.71
Naranga aenescens53.82
Kirkaldyia deyrollei62.04
Cicadellidae70.21

图7

不同场景下的模型检测结果"

图8

模型特征提取层可视化"

表3

不同种模型对昆虫的平均识别精度、检测速度和模型尺寸"

模型mAP/%检查速度/ms参数数量/ 百万模型体积/MB
YOLOv369.275361236
YOLOv3-TINY52.5132933
Faster R-CNN68.19325136532
本文70.9852830

图9

本文算法与YOLOv3算法预测结果对比"

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