中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页
机构
成果
学者
登录
注册
登陆
×
验证码:
换一张
忘记密码?
记住我
×
校外用户登录
CAS IR Grid
机构
南海海洋研究所 [2]
自动化研究所 [2]
采集方式
OAI收割 [4]
内容类型
期刊论文 [4]
发表日期
2024 [1]
2022 [1]
2009 [1]
2004 [1]
学科主题
Meteorolog... [2]
筛选
浏览/检索结果:
共4条,第1-4条
帮助
条数/页:
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
排序方式:
请选择
题名升序
题名降序
提交时间升序
提交时间降序
作者升序
作者降序
发表日期升序
发表日期降序
AiOENet: All-in-One Low-Visibility Enhancement to Improve Visual Perception for Intelligent Marine Vehicles Under Severe Weather Conditions
期刊论文
OAI收割
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2024, 卷号: 9, 期号: 2, 页码: 3811-3826
作者:
Liu, Ryan Wen
;
Lu, Yuxu
;
Guo, Yu
;
Ren, Wenqi
;
Zhu, Fenghua
  |  
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2024/09/09
Meteorology
Imaging
Navigation
Marine vehicles
Feature extraction
Visual perception
Snow
Deep neural network
intelligent marine vehicles
low-visibility enhancement
severe weather
visual perception
IRDCLNet: Instance Segmentation of Ship Images Based on Interference Reduction and Dynamic Contour Learning in Foggy Scenes
期刊论文
OAI收割
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 9, 页码: 6029-6043
作者:
Sun, Yuxin
;
Su, Li
;
Luo, Yongkang
;
Meng, Hao
;
Zhang, Zhi
  |  
收藏
  |  
浏览/下载:42/0
  |  
提交时间:2022/11/14
Marine vehicles
Image segmentation
Meteorology
Feature extraction
Interference
Object detection
Visualization
Foggy scene
ship instance segmentation
interference reduction module
dynamic contour learning
Marine Meteorology Research Progress of China from 2003 to 2006
期刊论文
OAI收割
ADVANCES IN ATMOSPHERIC SCIENCES, 2009, 卷号: 26, 期号: 1, 页码: 17-30
Wang, DX
;
Zhang, Y
;
Zeng, LL
;
Luo, L
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2011/07/03
marine meteorology
marine disaster
remote sensing
monitoring and forecasting technology
Progress in marine meteorology studies in China during 1999-2002
期刊论文
OAI收割
ADVANCES IN ATMOSPHERIC SCIENCES, 2004, 卷号: 21, 期号: 3, 页码: 485-496
Wang, DX
;
Qin, ZH
;
Shi, P
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2011/07/03
marine meteorology
China
field observation
marine disaster
remote sensing application