Yan Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yan Zhang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Guangyuan Zhang
School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
1 shared publication - Kefeng Li
School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
1 shared publication - Peng Wang
Shandong Jiaotong University, School of Information Science and Electrical Engineering (Artificial Intelligence College), Jinan, China
1 shared publication - Zhenfang Zhu
School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China
1 shared publication
Latest Publications
- Open Access
- 3 January 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (1) , 662
DFA-UNet: Efficient Railroad Image Segmentation
In computer vision technology, image segmentation is a significant technological advancement for the current problems of high-speed railroad image scene changes, low segmentation accuracy, and serious information loss. We propose a segmentation algorithm, DFA-UNet, based on an improved U-Net network architecture. The model uses the same encoder–decoder structure as U-Net. To be able to extract image features efficiently and further integrate the weights of each channel feature, we propose to embed the DFA attention module in the encoder part of the model for the adaptive adjustment of feature map weights. We evaluated the performance of the model on the RailSem19 dataset. The results showed that our model showed improvements of 2.48%, 0.22%, 3.31%, 0.97%, and 2.2% in mIoU, F1-score, Accuracy, Precision, and Recall, respectively, compared with U-Net. The model can effectively achieve the segmentation of railroad images.
- 3 January 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (1) , 662
Open AccessDFA-UNet: Efficient Railroad Image Segmentation
In computer vision technology, image segmentation is a significant technological advancement for the current problems of high-speed railroad image scene changes, low segmentation accuracy, and serious information loss. We propose a segmentation algorithm, DFA-UNet, based on an improved U-Net network architecture. The model uses the same encoder–decoder structure as U-Net. To be able to extract image features efficiently and further integrate the weights of each channel feature, we propose to embed the DFA attention module in the encoder part of the model for the adaptive adjustment of feature map weights. We evaluated the performance of the model on the RailSem19 dataset. The results showed that our model showed improvements of 2.48%, 0.22%, 3.31%, 0.97%, and 2.2% in mIoU, F1-score, Accuracy, Precision, and Recall, respectively, compared with U-Net. The model can effectively achieve the segmentation of railroad images.