基于并联卷积神经网络的水果品种识别

doi:10.3969/j.issn.1004-1524.2022.11.22

浙江农业学报 ›› 2022, Vol. 34 ›› Issue (11): 2533-2541.DOI: 10.3969/j.issn.1004-1524.2022.11.22

基于并联卷积神经网络的水果品种识别

李超(), 李锋(), 黄炜嘉

江苏科技大学电子信息学院,江苏镇江 212000

收稿日期:2020-11-16 出版日期:2022-11-25 发布日期:2022-11-29
通讯作者: 李锋
作者简介:*李锋,E-mail: lifengsl@126.com
李超(1994—),男,山东潍坊人,硕士,研究方向为机器视觉。E-mail: 1552022319@qq.com
基金资助:
国家自然科学基金(61671221)

Fruit variety recognition based on parallel convolutional neural network

LI Chao(), LI Feng(), HUANG Weijia

College of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212000, Jiangsu, China

Received:2020-11-16 Online:2022-11-25 Published:2022-11-29
Contact: LI Feng

摘要/Abstract

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

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.

Key words: image recognition, deep learning, boundary equilibrium generative adversarial network, convolution neural network

中图分类号:

S126
TP391

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.

LI Chao, LI Feng, HUANG Weijia. Fruit variety recognition based on parallel convolutional neural network[J]. Acta Agriculturae Zhejiangensis, 2022, 34(11): 2533-2541.

图/表 15

图1 卷积神经网络模型示意图

Fig.1 Schematic diagram of convolution neural network model

图2 改进的网络模型结构示意图

Fig.2 Schematic diagram of improved network model

图3 边界均衡生成对抗网络的结构示意图

Fig.3 Schematic diagram of boundary equilibrium generative adversarial network

图4 判别网络结构

Fig.4 Discriminating network structure

图5 生成网络结构

Fig.5 Generate network structure

图6 实验用水果品种的图像示意

Fig.6 Images of fruit varieties used in experiment

图7 迭代5 000次(a)和10 000次(b)后的结果

Fig.7 Result after 5 000 (a) and 10 000 (b) iterations

表1 BEGAN算法与其他数据增强算法的准确率对比

Table 1 Comparison of accuracy of data enhancement with BEGAN and traditional method %

类别 Category	传统数据增强 Traditional data enhancement	基于BEGAN的数据增强 Data enhancement via BEGAN
布瑞本苹果	93.25	95.51
Apple Braeburn
红雪苹果	93.40	95.49
Apple Crimson Snow
金色苹果2	93.26	96.13
Apple Golden2
金色苹果3	93.34	95.48
Apple Golden3
樱桃1 Cherry1	93.58	95.46
樱桃2 Cherry2	94.01	96.21
Rainer樱桃	94.12	95.45
Cherry Rainer
蜡黑樱桃	93.11	95.56
Cherry Wax Black

图8 不同样本数量对准确率的影响

Fig.8 Effect of different sample quantity on accuracy

图9 不同网络模型的准确率对比

Fig.9 Comparison of accuracy of different network models

图10 不同网络模型的损失值对比

Fig.10 Comparison of loss of different network models

图11 不同损失函数的准确率对比

Fig.11 Comparison of accuracy of models with different loss functions

图12 不同损失函数的损失值对比

Fig.12 Comparison of loss of models with different loss functions

表2 引入不同损失函数的识别准确率

Table 2 Accuracy of models with different loss functions %

类别 Category	旧损失函数 Old loss function	新损失函数 New loss function
布瑞本苹果Apple Braeburn	98.30	98.31
红雪苹果Apple Crimson Snow	97.59	99.50
金色苹果2 Apple Golden2	97.15	98.90
金色苹果3 Apple Golden3	97.80	97.85
樱桃1 Cherry1	98.41	99.45
樱桃2 Cherry2	97.73	98.79
Rainer樱桃Cherry Rainer	98.10	99.15
蜡黑樱桃Cherry Wax Black	97.86	98.86

表3 不同模型的准确率对比

Table 3 Comparison of accuracy of different algorithms %

模型 Model	准确率 Accuracy
舍去a通道的单只CNN Single CNN without channel a	97.10
特征加强+BP神经网络^[5] Feature enhancement+BP neural network	98.60
形状相似的水果自动识别研究^[11] Automatic recognition of fruits with similar shape	93.00
特征提取+单只CNN^[13] Feature extraction+single CNN	95.49
本文模型Proposed model	98.85

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基于并联卷积神经网络的水果品种识别

Fruit variety recognition based on parallel convolutional neural network

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