Sonar Image Generation by MFA-CycleGAN for Boosting Underwater Object Detection of AUVs-Reference-Cited by-同舟云学术

Sonar Image Generation by MFA-CycleGAN for Boosting Underwater Object Detection of AUVs

Published:2024-07 Issue:3 Volume:49 Page:905-919
ISSN:0364-9059
Container-title:IEEE Journal of Oceanic Engineering
language:
Short-container-title:IEEE J. Oceanic Eng.

Author:

Zhou Jianqun¹^ORCID,Li Yang¹^ORCID,Qin Hongmao¹^ORCID,Dai Pengwen²^ORCID,Zhao Zilong³^ORCID,Hu Manjiang¹^ORCID

Affiliation:

1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China

2. School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China

3. School of Social Sciences and Technology, Technical University of Munich, Munich, Germany

Funder

Defense Industrial Technology Development Program

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Major S&T Program of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/48/10599477/10472044.pdf?arnumber=10472044

Reference53 articles.

1. Autonomous Underwater Environment Perceiving and Modeling: An Experimental Campaign With FeelHippo AUV for Forward Looking Sonar-Based Automatic Target Recognition and Data Association

2. Automatic Target Recognition for Mine Countermeasure Missions Using Forward-Looking Sonar Data

3. Target Detection and Segmentation in Circular-Scan Synthetic Aperture Sonar Images Using Semisupervised Convolutional Encoder–Decoders

4. Object perception in underwater environments: a survey on sensors and sensing methodologies

5. An Underwater Image Enhancement Method for Simultaneous Localization and Mapping of Autonomous Underwater Vehicle

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Shuffle-RDSNet: a method for side-scan sonar image classification with residual dual-path shrinkage network;The Journal of Supercomputing;2024-05-29