Zedong Huang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zedong Huang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Jinan Gu
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China
1 shared publication - Jing Li
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China
1 shared publication - Junjie Hu
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China
1 shared publication - Shuwei Li
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China
1 shared publication
Latest Publications
- Open Access
- 7 June 2022
- Published by MDPI AG in Electronics
- Vol. 11 (12) , 1812
Depth Estimation of Monocular PCB Image Based on Self-Supervised Convolution Network
To improve the accuracy of using deep neural networks to predict the depth information of a single image, we proposed an unsupervised convolutional neural network for single-image depth estimation. Firstly, the network is improved by introducing a dense residual module into the encoding and decoding structure. Secondly, the optimized hybrid attention module is introduced into the network. Finally, stereo image is used as the training data of the network to realize the end-to-end single-image depth estimation. The experimental results on KITTI and Cityscapes data sets show that compared with some classical algorithms, our proposed method can obtain better accuracy and lower error. In addition, we train our models on PCB data sets in industrial environments. Experiments in several scenarios verify the generalization ability of the proposed method and the excellent performance of the model.
- 7 June 2022
- Published by MDPI AG in Electronics
- Vol. 11 (12) , 1812
Open AccessDepth Estimation of Monocular PCB Image Based on Self-Supervised Convolution Network
To improve the accuracy of using deep neural networks to predict the depth information of a single image, we proposed an unsupervised convolutional neural network for single-image depth estimation. Firstly, the network is improved by introducing a dense residual module into the encoding and decoding structure. Secondly, the optimized hybrid attention module is introduced into the network. Finally, stereo image is used as the training data of the network to realize the end-to-end single-image depth estimation. The experimental results on KITTI and Cityscapes data sets show that compared with some classical algorithms, our proposed method can obtain better accuracy and lower error. In addition, we train our models on PCB data sets in industrial environments. Experiments in several scenarios verify the generalization ability of the proposed method and the excellent performance of the model.