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基于并联卷积神经网络的水果品种识别
引用本文:李超,李锋,黄炜嘉. 基于并联卷积神经网络的水果品种识别[J]. 浙江农业学报, 2022, 34(11): 2533-2541. DOI: 10.3969/j.issn.1004-1524.2022.11.22
作者姓名:李超  李锋  黄炜嘉
作者单位:江苏科技大学 电子信息学院,江苏 镇江 212000
基金项目:国家自然科学基金(61671221)
摘    要:为了解决传统的水果图像识别算法在特征提取上的缺陷,以及传统卷积神经网络识别率低的问题,设计了一种基于并联卷积神经网络来提取水果特征的识别方法,利用ELU激活函数替代ReLU激活函数,利用最大类间距损失函数结合传统SoftmaxWithLoss损失函数来提高对相似品种的识别准确率。选取Fruit-360数据集中的8个品种,利用边界均衡生成对抗网络(BEGAN)结合传统的数据增强方法生成大量高质量的数据集,并用其进行训练。结果表明,该模型对8个品种的平均识别准确率达98.85%,具有良好的识别效果。

关 键 词:图像识别  深度学习  边界均衡生成对抗网络  卷积神经网络
收稿时间:2020-11-16

Fruit variety recognition based on parallel convolutional neural network
LI Chao,LI Feng,HUANG Weijia. Fruit variety recognition based on parallel convolutional neural network[J]. Acta Agriculturae Zhejiangensis, 2022, 34(11): 2533-2541. DOI: 10.3969/j.issn.1004-1524.2022.11.22
Authors:LI Chao  LI Feng  HUANG Weijia
Affiliation:College of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212000, Jiangsu, China
Abstract:In order to solve the defects of traditional fruit image recognition algorithms in feature extraction and the low recognition accuracy of traditional convolutional neural networks, a parallel convolutional neural network was proposed to extract fruit features. ELU activation function was introduced instead of ReLU activation function in the proposed model. Besides, a combination of maximum class spacing loss function and the traditional SoftmaxWithLoss loss function was designed to improve the recognition accuracy of similar varieties. The data of 8 fruit varieties in Fruit-360 data set was selected in the present study, and enhanced by the boundary equilibrium generative adversarial network (BEGAN) combined with the traditional data augmentation to generate a large number of high-quality data for model training. It was shown that the average recognition accuracy of 8 fruit varieties reached 98.85% and exhibited good recognition effect.
Keywords:image recognition  deep learning  boundary equilibrium generative adversarial network  convolution neural network  
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