基于区域卷积神经网络的农业害虫检测方法

摘要/Abstract

摘要： 当前农业害虫综合防治中,农业害虫检测主要通过专业人员手动收集和分类实地样本,这种手动分类方法既昂贵又耗时。现有的通过计算机实现的自动农业害虫检测对害虫所处背景环境的要求较高,并且无法实现农业害虫的定位。针对这些问题,文中基于深度学习的思想,提出了一种新的农业害虫自动检测方法,它由区域提取网络和Fast R-CNN两个部分组成。区域提取网络在任意大小且背景繁杂的图像上的某一个或多个区域进行特征提取,得到农业害虫的初步位置候选区;将农业害虫的初步位置候选区作为Fast R-CNN的输入,Fast R-CNN通过学习农业害虫种类的种内差异和种间相似性,判定初步位置候选区中的目标类别并计算精准坐标。文中同时建立了一个已标注标签的实际场景的农业害虫数据库,将提出的农业害虫检测方法在此数据库上进行测试,识别精度的均值可达到82.13%。实验结果表明,提出的方法能够有效地提升农业害虫类别判断的准确度,得到农业害虫的精准定位,优于以往的自动化农业害虫检测方法。

关键词: 害虫分类, 精准定位, 农业害虫数据库

Abstract: In the current integrated agricultural pest control,agricultural insect pests are detected primarily by professionals’ sample collection and sorting manually,such manual classification method is both expensive and time consuming.Existing computer-aided automatic detection of agricultural pests has a high requirement on the background environment of pests and cannot locate agricultural pests.To solve these problems,this paper proposed a new method for automatic detection of agricultural pests based on the idea of the deep learning.It contains the region proposal network and the Fast R-CNN network.Region proposal network extracts feature in one or more regions of arbitrary size and complicated background images,then gets preliminary position of the candidate regions of agricultural pests.Preliminary position of the candidate regions of agricultural pests is taken as an input to Fast R-CNN.Fast R-CNN finally learns the classification of target in the preliminary location candidate area and calculates exact coordinates by studying the intraspecific differences and interspecies similarity of agricultural pests.Meanwhile,this paper also established a labeled actual scene tag agricultural pests database,and the proposed method was tested on this database,with theaverage precision up to 82.13%.The experimental results show that the proposed method can effectively enhance the accuracy of agricultural pests detection,and get accurate positions,and is superior to the previous automated agricultural pest detection methods.

Key words: Agricultural pest database, Pest Classification, Precise positioning

中图分类号:

TP301

魏杨, 毕秀丽, 肖斌. 基于区域卷积神经网络的农业害虫检测方法[J]. 计算机科学, 2018, 45(11A): 226-229. https://doi.org/

WEI Yang, BI Xiu-li, XIAO Bin. Agricultural Insect Pest Detection Method Based on Regional Convolutional Neural Network[J]. Computer Science, 2018, 45(11A): 226-229. https://doi.org/

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