[1]张善文,许新华,齐国红.基于孪生多尺度空洞胶囊网络的黄瓜叶部病害检测方法[J].江苏农业学报,2023,(09):1827-1833.[doi:doi:10.3969/j.issn.1000-4440.2023.09.004]
　ZHANG Shan-wen,XU Xin-hua,QI Guo-hong.Cucumber leaf disease detection based on Siamese multi-scale dilated capsule network[J].,2023,(09):1827-1833.[doi:doi:10.3969/j.issn.1000-4440.2023.09.004]
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基于孪生多尺度空洞胶囊网络的黄瓜叶部病害检测方法()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2023年09期
页码:
1827-1833
栏目:
植物保护
出版日期:
2023-12-31

文章信息/Info

Title:
Cucumber leaf disease detection based on Siamese multi-scale dilated capsule network
作者:
张善文; 许新华; 齐国红
(郑州西亚斯学院电子信息工程学院,河南郑州451150)
Author(s):
ZHANG Shan-wen; XU Xin-hua; QI Guo-hong
(School of Electronics Information Engineering, Zhengzhou SIAS University, Zhengzhou 451150, China)
关键词:
黄瓜病害; 孪生网络; 多尺度空洞卷积; 胶囊网络; 孪生多尺度空洞胶囊网络
Keywords:
cucumber disease; Siamese network; multi-scale dilated convolution; capsule network; Siamese multi-scale dilated capsule network (SMSDCNet)
分类号:
TP391.41；S642.2
DOI:
doi:10.3969/j.issn.1000-4440.2023.09.004
文献标志码:
A
摘要:
在黄瓜叶部病害检测中,传统方法简单但检测正确率低,难以处理多种多样的病害叶片图像,深度卷积网络的检测正确率高,但依赖于大量训练样本,训练时间长。本研究提出一种基于孪生多尺度空洞胶囊网络(Siamese multi-scale dilated capsule network, SMSDCNet)的黄瓜叶部病害检测方法,该方法整合了孪生网络、空洞卷积网络和胶囊网络的优势,将多尺度空洞卷积模块Inception引入胶囊网络,作为孪生网络的子网络,构建孪生多尺度空洞胶囊网络模型,提取多尺度判别特征,再进行矢量化处理,最后经动态路由算法得到具有空间位置信息的胶囊向量,进行病害检测与识别。SMSDCNet克服了深度卷积网络需要大量训练样本、训练时间长以及对旋转和仿射变换敏感的问题,并且克服了多尺度卷积网络训练参数较多的问题。在一个较小的黄瓜病害叶片图像数据集上进行试验,病害检测精度达90%以上。结果表明,该方法能够实现小训练样本集的黄瓜叶部病害检测,为训练样本有限情况下的作物病害检测提供了一种新方法。
Abstract:
In cucumber leaf disease detection, the traditional methods are simple but low detection accuracy, and they are difficult to deal with the various diseased leaf images. Deep convolution neural networks (CNNs) have high detection accuracy, but they rely on a large number of training samples, and the training time is long. A cucumber leaf disease detection method based on Siamese multi-scale dilated capsule network (SMSDCNet) was proposed. It integrated the advantages of Siamese network, dilated convolution network and capsule network (CapsNet). In SMSDCNet, the multi-scale dilated convolution inception module was introduced into CapsNet to construct the two sub-networks for Siamese multi-scale dilated capsule network model, then the multi-scale discriminant features were extracted and vectorized. Finally, the capsule vector with spatial location information was obtained through the dynamic routing algorithm for detecting and recognizing cucumber leaf diseases. SMSDCNet overcame the problems of deep convolutional networks that required a large number of training samples, long training time, and sensitivity to rotation and affine transformation, and overcame the problem that multi-scale convolutional networks required more training parameters. Disease detection experiments were conducted on a small cucumber disease leaf image dataset. The detection accuracy was more than 90%. The results showed that the proposed method could detect cucumber leaf disease with small training sample set, which provided a new method for disease detection under the condition of limited training samples.

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备注/Memo

备注/Memo:
收稿日期:2022-11-12基金项目:国家自然科学基金项目(62172338)；河南省科技厅科技攻关项目(222102110134)；河南省高等学校重点科研项目(22B520049)作者简介:张善文(1965-),男,陕西西安人,博士,教授,主要从事人工智能在精准农业中的应用研究。(E-mail)wjdw716@163.com
更新日期/Last Update: 2024-01-15