Lanxi Min
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Lanxi Min has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Boyan Fan
School of Computer and Information Engineering, Xiamen Institute of Technology, Xiamen 361024, China
1 shared publication - Chiming Wang
Xiamen University of Technology
1 shared publication - Jing Zhang
School of Computer and Information Engineering, Xiamen Institute of Technology, Xiamen 361024, China
1 shared publication - Jingjing Xiao
Xiamen University of Technology
1 shared publication - Jiuhu Chen
Fujian Xinji Shipping Service Co. Ltd., Xiamen 361000, China
1 shared publication - Rongjiong Wu
School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
1 shared publication
Latest Publications
- Open Access
- 26 June 2023
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 11 (7) , 1298
Study on the Classification Perception and Visibility Enhancement of Ship Navigation Environments in Foggy Conditions
Based on ship navigational requirements and safety in foggy conditions and with a particular emphasis on avoiding ship collisions and improving navigational abilities, we constructed a fog navigation dataset along with a new method for enhancing foggy images and perceived visibility using a discriminant deep learning architecture and the EfficientNet neural network by replacing the SE module and incorporating a convolution block attention module and focal loss function. The accuracy of our model exceeded 95%, which meets the needs of an intelligent ship navigation environment in foggy conditions. As part of our research, we also determined the best enhancement algorithm for each type of fog according to its classification.
- 26 June 2023
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 11 (7) , 1298
Open AccessStudy on the Classification Perception and Visibility Enhancement of Ship Navigation Environments in Foggy Conditions
Based on ship navigational requirements and safety in foggy conditions and with a particular emphasis on avoiding ship collisions and improving navigational abilities, we constructed a fog navigation dataset along with a new method for enhancing foggy images and perceived visibility using a discriminant deep learning architecture and the EfficientNet neural network by replacing the SE module and incorporating a convolution block attention module and focal loss function. The accuracy of our model exceeded 95%, which meets the needs of an intelligent ship navigation environment in foggy conditions. As part of our research, we also determined the best enhancement algorithm for each type of fog according to its classification.