IRDCLNet: Instance Segmentation of Ship Images Based on Interference Reduction and Dynamic Contour Learning in Foggy Scenes-Reference-Cited by-同舟云学术

IRDCLNet: Instance Segmentation of Ship Images Based on Interference Reduction and Dynamic Contour Learning in Foggy Scenes

Published:2022-09 Issue:9 Volume:32 Page:6029-6043
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Sun Yuxin¹^ORCID,Su Li¹^ORCID,Luo Yongkang²^ORCID,Meng Hao¹,Zhang Zhi¹^ORCID,Zhang Wen¹^ORCID,Yuan Shouzheng¹^ORCID

Affiliation:

1. College of Intelligent Systems Science and Engineering and the Key Laboratory of Intelligent Technology and Application of Marine Equipment, Ministry of Education, Harbin Engineering University, Harbin, China

2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

Funder

National Key Research and Development Program of China

Project of Intelligent Situation Awareness System for Smart Ship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/9875133/09722867.pdf?arnumber=9722867

Reference50 articles.

1. A Data Set for Airborne Maritime Surveillance Environments

2. Image-based ship detection and classification for unmanned surface vehicle using real-time object detection neural networks;lee;Proc 28th Int Ocean Polar Eng Conf,2018

3. HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery

4. HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation

5. SOLO: Segmenting Objects by Locations

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