Kunhai Xu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Kunhai Xu has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Changming Zhu
College of Information Engineering, Shanghai Maritime University, Shanghai, China
1 shared publication - Enguang Guan
Shanghai Maritime University
1 shared publication - Yihai Liu
Jiangsu Automation Research Institute, Lianyungang 222061, China
1 shared publication - Yilin Dong
College of Information Engineering, Shanghai Maritime University, Shanghai, China
1 shared publication
Latest Publications
- Open Access
- 7 August 2023
- Published by MDPI AG in Remote Sensing
- Vol. 15 (15) , 3916
E-FPN: Evidential Feature Pyramid Network for Ship Classification
Ship classification, as an important problem in the field of computer vision, has been the focus of research for various algorithms over the past few decades. In particular, convolutional neural networks (CNNs) have become one of the most popular models for ship classification tasks, especially using deep learning methods. Currently, several classical methods have used single-scale features to tackle ship classification, without paying much attention to the impact of multiscale features. Therefore, this paper proposes a multiscale feature fusion ship classification method based on evidence theory. In this method, multiple scales of features were utilized to fuse the feature maps of three different sizes (40 × 40 × 256, 20 × 20 × 512, and 10 × 10 × 1024), which were used to perform ship classification tasks separately. Finally, the multiscales-based classification results were treated as pieces of evidence and fused at the decision level using evidence theory to obtain the final classification result. Experimental results demonstrate that, compared to classical classification networks, this method can effectively improve classification accuracy.
- 7 August 2023
- Published by MDPI AG in Remote Sensing
- Vol. 15 (15) , 3916
Open AccessE-FPN: Evidential Feature Pyramid Network for Ship Classification
Ship classification, as an important problem in the field of computer vision, has been the focus of research for various algorithms over the past few decades. In particular, convolutional neural networks (CNNs) have become one of the most popular models for ship classification tasks, especially using deep learning methods. Currently, several classical methods have used single-scale features to tackle ship classification, without paying much attention to the impact of multiscale features. Therefore, this paper proposes a multiscale feature fusion ship classification method based on evidence theory. In this method, multiple scales of features were utilized to fuse the feature maps of three different sizes (40 × 40 × 256, 20 × 20 × 512, and 10 × 10 × 1024), which were used to perform ship classification tasks separately. Finally, the multiscales-based classification results were treated as pieces of evidence and fused at the decision level using evidence theory to obtain the final classification result. Experimental results demonstrate that, compared to classical classification networks, this method can effectively improve classification accuracy.