MobileNetV2 Algorithm for Water Microfiber Image Recognition
[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
42卷
Number of periods:
2021 05
Page number:
25-31
Column:
Public date:
2021-09-10
- Title:
- MobileNetV2 Algorithm for Water Microfiber Image Recognition
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2021.05.005
- 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.
- References:
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Last Update:
2021-10-11