基于图像梯度的数据增广方法

doi:10.3969/j.issn.0255-8297.2021.02.012

Abstract

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

CLC Number:

TP181

LIU Zhiyu, ZHANG Shufen, LIU Yang, LUO Changyin, LI Min. Data Augmentation Method Based on Image Gradient[J]. Journal of Applied Sciences, 2021, 39(2): 302-311.

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Data Augmentation Method Based on Image Gradient

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