腐蚀与防护2024,Vol.45Issue(9) :57-66.DOI:10.11973/fsyfh240330

深度学习在水下目标检测与腐蚀评估中的应用进展

Application Advance of Deep Learning in Underwater Target Detection and Corrosion Assessment

杨进 李阳 曾辉 张海龙 朱庆军 段继周
腐蚀与防护2024,Vol.45Issue(9) :57-66.DOI:10.11973/fsyfh240330

深度学习在水下目标检测与腐蚀评估中的应用进展

Application Advance of Deep Learning in Underwater Target Detection and Corrosion Assessment

杨进 1李阳 2曾辉 1张海龙 1朱庆军 2段继周2
扫码查看

作者信息

  • 1. 中国长江电力股份有限公司,宜昌 443002
  • 2. 中国科学院海洋研究所海洋环境腐蚀与牛物污损重点实验室,青岛 266071;重庆交通大学航运与船舶工程学院,重庆 400074
  • 折叠

摘要

随着海洋资源的不断开发和海洋环境的持续变化,水下目标检测和腐蚀评估在海洋工程、海洋资源开发和水下基础设施维护等领域发挥着重要作用.两种技术虽路线不同却有交叉,在一定程度上是相互关联的.综述了近年来深度学习技术在水下目标检测与腐蚀评估中的应用现状,首先介绍了水下目标检测和腐蚀评估的背景,然后分别对深度学习技术在水下目标检测和腐蚀评估方面的应用现状进行了简要分析,最后讨论了目前水下目标检测和腐蚀评估在深度学习的影响下面临的挑战以及未来的研究方向.

Abstract

With the continuous development of ocean resources and the ongoing changes in the marine environment,underwater target detection and corrosion assessment have played a significant role in fields such as marine engineering,ocean resource exploitation,and maintenance of underwater infrastructure.Although these two technologies follow different paths,they intersect and were found to be interrelated to a certain extent.The current developments of the application of deep learning technology in underwater target detection and corrosion assessment in recent years were reviewed.It introduced the background of underwater target detection and corrosion assessment was introduced,and then the current state of application of deep learning technology in both underwater target detection and corrosion assessment was analyzed.Finally,the potential challenges of underwater target detection and corrosion assessment under the influence of deep learning was discussed,as well as future research directions.

关键词

水下目标检测/水下腐蚀检测/图像增强/深度学习

Key words

underwater target detection/underwater corrosion detection/image enhancement/deep learning

引用本文复制引用

基金项目

中国长江电力股份有限公司科研项目(2223020003)

出版年

2024
腐蚀与防护
上海市腐蚀科学技术学会 上海材料研究所

腐蚀与防护

CSTPCDCSCD北大核心
影响因子:0.462
ISSN:1005-748X
参考文献量10
段落导航相关论文