期刊论文

A detecting method for the rape pests based on deep convolutional neural network

中文

湖南农业大学学报(自然科学版)

1007-1032

2019

45

5

560-564

10.13331/j.cnki.jhau.2019.05.019

61772031:国家自然科学基金 2017XK2054:湖南省长株潭国家自主创新示范区专项 12B061:湖南省教育厅优秀青年项目 SYL201802002:湖南农业大学双一流建设项目

本校为第一机构

针对目前油菜虫害识别在背景、角度、姿态、光照等方面的鲁棒性问题,提出一种基于深度卷积神经网络的油菜虫害检测方法:首先在卷积神经网络和区域候选网络的基础上,构建油菜虫害检测模型,再在深度学习tensorflow框架上实现模型的检测,最后对比分析结果。油菜虫害检测模型利用VGG16网络提取油菜虫害图像的特征,区域候选网络生成油菜害虫的初步位置候选框,Fast R–CNN实现候选框的分类和定位。结果表明,该方法可实现对蚜虫、菜青虫(幼虫)、菜蝽、跳甲、猿叶甲5种油菜害虫的快速准确检测,平均准确率达94.12%,与RCNN、 Fast R–CNN、多特征融合方法、颜色特征提取方法相比,准确率分别提高了28%、23%...

Aiming at the robustness of rape pest identification methods in terms of background, angle, posture and illumination, a method was proposed based on deep convolutional neural network to detect rape pests. Firstly, the detection model of rape pest was constructed on the basis of convolutional neural network and region proposal network. Secondly, the model was tested on the deep learning tensor flow framework. Finally, the experimental results were compared and analyzed. Based on convolutional neural networks and regional candidate networks, a rape pest detection model was constructed by using t...