[1]吕璐璐,陈树越,王利平,等.水体微纤维图像识别的改进MobileNetV2算法[J].郑州大学学报(工学版),2021,42(05):25-31.[doi:10.13705/j.issn.1671-6833.2021.05.005]
　Lu Lulu,Chen Shuyue,Wang Liping,et al.MobileNetV2 Algorithm for Water Microfiber Image Recognition[J].Journal of Zhengzhou University (Engineering Science),2021,42(05):25-31.[doi:10.13705/j.issn.1671-6833.2021.05.005]
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水体微纤维图像识别的改进MobileNetV2算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42卷
期数:
2021年05期
页码:
25-31
栏目:
出版日期:
2021-09-10

文章信息/Info

Title:
MobileNetV2 Algorithm for Water Microfiber Image Recognition
作者:
吕璐璐; 陈树越; 王利平; 许霞
常州大学微电子与控制工程学院;常州大学环境与安全工程学院;
Author(s):
Lu Lulu; Chen Shuyue; Wang Liping; Xu Xia;
School of Microelectronics and Control Engineering, Changzhou University; School of Environment and Safety Engineering, Changzhou University;
关键词:
Keywords:
waterbody;  microfiber recognition;  MobileNetV2;  pooling fusion;  feature reconstruction
DOI:
10.13705/j.issn.1671-6833.2021.05.005
文献标志码:
A
摘要:
针对目前人工识别水体微纤维耗时耗力和传统图像处理算法鲁棒性弱等问题，构建了一种改进MobileNetV2网络识别微纤维算法。在特征提取部分采用特征重构策略，压缩深度卷积特征，提取全局卷积特征信息，利用多层全连接学习深度卷积特征的重要性并减少网络参数的计算，然后分配权重完成微纤维特征的重构。此外，采用不同大小的下采样器捕获不同尺度的特征信息并融合，增强微纤维图像的关键信息，提升模型对微纤维图像的学习能力与识别微纤维的效果。实验结果表明，改进MobileNetV2网络的微纤维识别准确率达到0.9796，与原始MobileNetV2网络相比较，识别准确率提高了0.0254，同时，误识率和漏识率也有显著的降低。
Abstract:
Aiming at the problems of time-consuming and labor-consuming manual identification of water microfibers, and the weak robustness of traditional image processing algorithms for identifying water microfiber images, an improved MobileNetV2 network identification method for microfibers is constructed. In the feature extraction part, the feature reconstruction strategy is adopted. Firstly, the deep convolution features are compressed to obtain the global receptive field. Then, the fully connected layers are used to generate weights for each channel to establish the interdependence between the channels. Finally, the channel is weighted to the original in terms of features to complete the reconstruction of the original features. In addition, different sizes of downsamplers are used to capture and fuse feature information of different scales to enhance the detailed feature information of microfibers, and to improve the model′s learning ability and recognition effect of microfibers. The improved MobileNetV2 network′s microfiber recognition accuracy rate reaches 97.96%. Compared with the original MobileNetV2 network, the recognition accuracy rate is increased by 2.54%. At the same time, the false recognition rate and the missed recognition rate are also significantly reduced. In comparison to ResNet, DenseNet, VGG16 and NasNet networks, the model size is compressed several times, the accuracy of microfiber recognition is improved, and the false recognition rate and missed recognition rate are greatly reduced. Experimental results show that the network model can extract more complete feature information for microfiber. While strengthening the microfiber feature to identify the directivity, the model is reduced, and the difficulty of deployment in mobile devices is reduced as well. The improved model recognizes microfibers with higher accuracy and better stability.
更新日期/Last Update: 2021-10-11