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应用科学学报 >

2021 , Vol. 39 >Issue 2: 302 - 311

DOI: https://doi.org/10.3969/j.issn.0255-8297.2021.02.012

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基于图像梯度的数据增广方法

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  • 1. 华北理工大学 理学院, 河北 唐山 063210;
    2. 河北省数据科学与应用重点实验室, 河北 唐山 063210;
    3. 唐山市数据科学重点实验室, 河北 唐山 063210

收稿日期: 2020-08-28

  网络出版日期: 2021-04-01

基金资助

唐山市重点研发计划项目（No.18120203A）资助

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Data Augmentation Method Based on Image Gradient

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  • 1. College of Science, North China University of Science and Technology, Tangshan 063210, Hebei, China;
    2. Hebei Key Laboratory of Data Science and Application, Tangshan 063210, Hebei, China;
    3. Tangshan Key Laboratory of Data Science, Tangshan 063210, Hebei, China

Received date: 2020-08-28

  Online published: 2021-04-01

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摘要

卷积神经网络用于图像识别的分类任务，需要大规模的图像数据集进行训练。因需要采集目标图像数量和设备条件的限制，采用常规方法难以获取足够多的图像样本，且耗时耗力耗财。目前已提出了多种多样的样本增广方法来解决图像样本不足的问题，本文介绍了数据增广的研究背景和意义。以提高卷积神经网络的图像识别的准确率为目的，针对图像数据增广提出了基于图像梯度的数据增广方法。选取最大图像梯度值，通过精准裁剪方法增加图像样本，扩增图像数据集，使用增广后的数据集对卷积神经网络进行训练。应用Tensorflow深度学习框架和VGG16网络模型，选取PlantVillage的部分数据集，将训练集数据增广至原来的6倍，对扩增前后的训练集进行训练和对比。实验结果表明：使用数据增广后训练集训练的模型的准确率提升4.18%。

关键词： 数据增广; 图像梯度; 卷积神经网络; Tensorflow深度学习框架; PlantVillage数据集

本文引用格式

刘之瑜, 张淑芬, 刘洋, 罗长银, 李敏 . 基于图像梯度的数据增广方法[J]. 应用科学学报, 2021 , 39(2) : 302 -311 . DOI: 10.3969/j.issn.0255-8297.2021.02.012

Abstract

As used in classification of image recognition, convolutional neural network requires large-scale image data set for training. Due to the limitation of the number of target images to be collected and the conditions of image acquisition equipment, it is difficult to obtain enough image samples by conventional methods because of time-consuming, laborconsuming and money-consuming. In order to solve the insufficiency of image samples, a variety of sample enlargement methods have been proposed. This paper introduces the research background and significance of data augmentation. For the purpose of improving the accuracy of image recognition of convolutional neural network, a data augmentation method based on image gradient is proposed. The image gradient is selected to increase image sample and enlarge image data set by precise clipping method, and the convolutional neural network is trained with the expanded data set. By using Tensorflow deep learning framework and VGG16 network model, and selecting some data sets of PlantVillage, the training set data can be expanded to 6 times of the original. The training set before and after the expansion is trained and compared. Experimental results show that the accuracy rate of the model trained by the training set after data augmentation is increased by 4.18%.

Key words： data augmentation; image gradient; convolutional neural network; TensorFlow deep learning framework; PlantVillage dataset

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