Xiao Hu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiao Hu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Dongjian He
College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
1 shared publication - Jingze Feng
College of Information Engineering, Northwest A&F University, Yangling 712100, Shanxi, China
1 shared publication - Meili Wang
Shaanxi Joint Laboratory of Artificial Intelligence, Xi’an University, Xi’an 710065, China
1 shared publication - Xiaofei Chao
College of Information Engineering, Northwest A & F University, Yangling, China
1 shared publication - Zhao Zhang
College of Information Engineering, Northwest A&F University, Yangling 712100, Shanxi, China
1 shared publication
Latest Publications
- Open Access
- 18 May 2021
- Published by MDPI AG in Applied Sciences
- Vol. 11 (10) , 4614
Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
The fast and accurate identification of apple leaf diseases is beneficial for disease control and management of apple orchards. An improved network for apple leaf disease classification and a lightweight model for mobile terminal usage was designed in this paper. First, we proposed SE-DEEP block to fuse the Squeeze-and-Excitation (SE) module with the Xception network to get the SE_Xception network, where the SE module is inserted between the depth-wise convolution and point-wise convolution of the depth-wise separable convolution layer. Therefore, the feature channels from the lower layers could be directly weighted, which made the model more sensitive to the principal features of the classification task. Second, we designed a lightweight network, named SE_miniXception, by reducing the depth and width of SE_Xception. Experimental results show that the average classification accuracy of SE_Xception is 99.40%, which is 1.99% higher than Xception. The average classification accuracy of SE_miniXception is 97.01%, which is 1.60% and 1.22% higher than MobileNetV1 and ShuffleNet, respectively, while its number of parameters is less than those of MobileNet and ShuffleNet. The minimized network decreases the memory usage and FLOPs, and accelerates the recognition speed from 15 to 7 milliseconds per image. Our proposed SE-DEEP block provides a choice for improving network accuracy and our network compression scheme provides ideas to lightweight existing networks.
- 18 May 2021
- Published by MDPI AG in Applied Sciences
- Vol. 11 (10) , 4614
Open AccessConstruction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
The fast and accurate identification of apple leaf diseases is beneficial for disease control and management of apple orchards. An improved network for apple leaf disease classification and a lightweight model for mobile terminal usage was designed in this paper. First, we proposed SE-DEEP block to fuse the Squeeze-and-Excitation (SE) module with the Xception network to get the SE_Xception network, where the SE module is inserted between the depth-wise convolution and point-wise convolution of the depth-wise separable convolution layer. Therefore, the feature channels from the lower layers could be directly weighted, which made the model more sensitive to the principal features of the classification task. Second, we designed a lightweight network, named SE_miniXception, by reducing the depth and width of SE_Xception. Experimental results show that the average classification accuracy of SE_Xception is 99.40%, which is 1.99% higher than Xception. The average classification accuracy of SE_miniXception is 97.01%, which is 1.60% and 1.22% higher than MobileNetV1 and ShuffleNet, respectively, while its number of parameters is less than those of MobileNet and ShuffleNet. The minimized network decreases the memory usage and FLOPs, and accelerates the recognition speed from 15 to 7 milliseconds per image. Our proposed SE-DEEP block provides a choice for improving network accuracy and our network compression scheme provides ideas to lightweight existing networks.