[1]赵 洋,梁迎春,许 军,等.改进 ResNet18 网络模型的花卉识别[J].计算机技术与发展,2022,32(07):167-172.[doi:10. 3969 / j. issn. 1673-629X. 2022. 07. 029]
　ZHAO Yang,LIANG Ying-chun,XU Jun,et al.Flower Recognition Based on Improved ResNet18 Network Model[J].,2022,32(07):167-172.[doi:10. 3969 / j. issn. 1673-629X. 2022. 07. 029]
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改进 ResNet18 网络模型的花卉识别()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年07期
页码:
167-172
栏目:
应用前沿与综合
出版日期:
2022-07-10

文章信息/Info

Title:
Flower Recognition Based on Improved ResNet18 Network Model
文章编号:
1673-629X(2022)07-0167-06
作者:
赵 洋; 梁迎春; 许 军; 李大舟
1. 沈阳化工大学 计算机科学与技术学院,辽宁 沈阳 110142;
2. 辽宁省化工过程工业智能化技术重点实验室,辽宁 沈阳 110142
Author(s):
ZHAO Yang1; 2 ; LIANG Ying-chun1 ; XU Jun1 ; LI Da-zhou1; 2
1. School of Computer Science and Technology,Shenyang University of Chemical Technology,Shenyang 110142,China;
2. Key Laboratory of Industrial Intelligence Technology on Chemical Process of Liaoning Province,Shenyang 110142, China
关键词:
ResNet18; 注意力机制; 空洞卷积; 花卉识别; 深度学习
Keywords:
ResNet18; attention mechanism; dilated convolution; flower recognition; deep learning
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 07. 029
摘要:
花卉识别在生活中有重要的应用和研究价值。 目前,传统的花卉识别方法都是通过人工手动选择多个特征进行分类, 存在识别准确率低、泛化能力较弱和分类时间长等问题。 由于不同的花朵之间存在相似度,通过对每张图片随机变化,增强数据集,把花卉作为研究对象, 提出了一种基于 ResNet18 网络模型优化的花卉识别方法。 将 ResNet18 网络模型中残差块的基础 卷积替换为空洞卷积,提取花卉图片更多的细节特征来实现高精度,接着在每个残差块后分别加入经过改进的通道注意力机制优化网络权重,构造改进的 ResNet18 网络模型,在 Oxford 102 Flowers 牛津花卉数据集上的实验进行了仿真。 实验结果显示,在 Oxford 102 Flowers 牛津花卉数据集上 ResNet 网络模型相较于基础 AlexNet、VGG-16 网络模型准确率高。改进的 ResNet? 网络模型识别精度可以高达 97. 78% ,比仅使用空洞卷积的模型提高了 3. 11 个百分点,比原模型提高了 4. 45 个百分点。 改进的 ResNet18 网络模型在花卉识别的泛化和拟合能力有显著的提高。
Abstract:
Flower recognition has important application and research value in life. At present,the traditional flower recognition methods select multiple features manually for classification, which has some problems, such as low recognition accuracy, weak generalization ability and long classification time. Due to the similarity between different flowers,by randomly changing each picture and enhancing the data set,taking flowers as the research object,a flower recognition method based on ResNet18 network model optimization is proposed.The basic convolution of residual blocks in ResNet18 network model is replaced by void convolution,and more detailed features of flower pictures are extracted to achieve high precision. Then,after each residual block,an improved channel attention mechanism is added to optimize the network weight, and an improved ResNet18 network model is constructed. The experiment is simulated on Oxford 102Flowers data set. The experimental results show that the accuracy of ResNet network model is higher than that of basic AlexNet and VGG-16 network models on Oxford 102 Flowers data set. The recognition accuracy of the improved ResNet network model can be as high as 97. 78% ,which is 3. 11 percentage points higher than the model using only hole convolution and 4. 45 percentage points higher than the original model. The generalization and fitting ability of the improved ResNet18 network model in flower recognition are significantly improved.

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更新日期/Last Update: 2022-07-10