| 基于并联卷积神经网络的水果品种识别 |
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| 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 |
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| 作者姓名： | 李超 李锋 黄炜嘉 |
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| 作者单位： | 江苏科技大学 电子信息学院,江苏 镇江 212000 |
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| 基金项目： | 国家自然科学基金(61671221) |
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| 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。
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| 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 |
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| 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 |
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| Authors: | LI Chao LI Feng HUANG Weijia |
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| Affiliation: | College of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212000, Jiangsu, China |
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| 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. |
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| Keywords: | image recognition deep learning boundary equilibrium generative adversarial network convolution neural network |
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