基于深度学习的水下图像目标检测综述

罗逸豪; 刘奇佩; 张吟; 周河宇; 张钧陶; 曹翔

doi:10.11999/JEIT221402

基于深度学习的水下图像目标检测综述

doi: 10.11999/JEIT221402

1.
宜昌测试技术研究所宜昌 443003
2.
军事科学院系统工程研究院北京 100141
3.
长沙学院长沙 410022

详细信息

作者简介:
罗逸豪：男，博士，研究方向为深度学习、计算机视觉、声呐图像处理

刘奇佩：男，博士，研究方向为水声信号处理、声呐图像处理

张吟：男，高级工程师，研究方向为声呐图像处理

周河宇：男，博士，研究方向为深度学习、计算机视觉

张钧陶：男，工程师，研究方向为深度学习、计算机视觉

曹翔：男，博士，研究方向为深度学习、计算机视觉

通讯作者:
罗逸豪　luoyihao@hust.edu.cn

中图分类号: TN911.73; TP391.4
计量
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- PDF下载量: 622
- 被引次数: 0
出版历程
- 收稿日期: 2022-11-08
- 修回日期: 2023-04-10
- 网络出版日期: 2023-04-24
- 刊出日期: 2023-10-31

Review of Underwater Image Object Detection Based on Deep Learning

1.
Yichang Testing Technique Research Institute, Yichang 443003, China
2.
Institute of System Engineering, AMS, PLA, Beijing 100141, China
3.
Changsha University, Changsha 410022, China

摘要

摘要: 水下图像目标检测是水下智能化探测的核心技术之一，广泛应用于工业及军事领域。深度学习相关技术的突破为水下图像目标检测的发展带来了新的机遇，但是目前该领域的综述较为陈旧，并且缺乏一定的系统性和全面性。该文对基于深度学习的水下可见光图像和声呐图像目标检测研究工作进行了详细总结与分析。首先，对基于深度学习的通用目标检测算法框架进行了梳理，包含骨干网络、颈部模块、检测头部、训练算法、推理策略、数据集6项要素，并系统性地总结了每个要素存在的问题及最新研究工作；然后，调研了水下可见光图像目标检测最新进展，分别从数据集发展、模型设计、训练算法进行总结；同时，归纳并分析了水下声呐图像目标检测相关工作，包含前视、侧扫、合成孔径3种声呐。最后，结合深度学习最新研究探讨了该领域的研究趋势。
- 水下图像目标检测 /
- 深度学习 /
- 可见光图像 /
- 声呐图像 /
- 数据集
Abstract: Underwater image object detection is one of the core technologies of underwater intelligent exploration, which is widely used in industrial and military fields. The breakthrough of deep learning related technologies has brought new opportunities for the development of underwater image object detection, but the current reviews are relatively old and lack a certain degree of systematicness and comprehensiveness. In this paper, the research of underwater visible and sonar image detection based on deep learning is summarized and analyzed in detail. Firstly, the general object detection algorithm framework based on deep learning is sorted out, including six elements: backbone, neck, head, training algorithm, inference strategy, and evaluation criteria, and the problems of each element and the latest research work are systematically summarized; Then, the latest progresses of underwater visible image object detection are investigated and summarized from three aspects: data set, model design, and training method; Meanwhile, the works related to underwater sonar image detection are summarized and analyzed, including forward-looking sonar, side-scanning sonar and synthetic aperture sonar. Finally, the research trend of underwater image object detection is discussed based on the latest research on deep learning.
- Underwater image object detection /
- Deep learning /
- Visible image /
- Sonar image /
- Data set

HTML全文

图 1 基于深度学习的目标检测模型

下载: 全尺寸图片幻灯片

表 1 可用于水下可见光图像目标检测的数据集

数据集	训练集图像数	测试集图像数	类别数	类别描述	用途	年份
URPC2017^[53]	17655	985*	3	海参、海胆、扇贝	目标检测	2017
URPC2018^[53]	2901	800*	4	海参、海胆、扇贝、海星	目标检测	2018
URPC2019^[53]	4757	1029*	4	海参、海胆、扇贝、海星	目标检测	2019
URPC2020-ZJ^[53]	5543	2000*	4	海参、海胆、扇贝、海星	目标检测	2020
URPC2020-DL^[53]	6575	2400*	4	海参、海胆、扇贝、海星	目标检测	2020
URPC2021^[53]	7600	2400*	4	海参、海胆、扇贝、海星	目标检测	2021
RUIE-UHTS^[54]	300	–	3	海参、海胆、扇贝	目标检测	2020
UDD^[55]	1827	400	3	海参、海胆、扇贝	目标检测	2022
UWD^[56]	10000	–	4	海参、海胆、扇贝、海星	目标检测	2020
DUO^[57]	6671	1111	4	海参、海胆、扇贝、海星	目标检测	2021
Fish4Knowledge^[58]	27370	–	23	海底鱼类	目标检测	2013
Brackish^[59]	14518	–	6	大鱼、小鱼、水母、螃蟹等	目标检测	2019
Marine Litter^[60]	5720	–	3	塑料垃圾、人为目标、生物	目标检测	2019
TrashCan^[61]	7212	–	22	海底垃圾、动植物等	目标检测/分割	2020
SUIM^[62]	1525	110	8	鱼类、珊瑚、植物、人、残骸等	目标检测/分割	2020
Kyutech10K^[63]	10728	–	7	虾、鱿鱼、螃蟹、鲨鱼等	图像分类	2018
UIEB^[64]	950	–	8	各类珊瑚与海洋生物等	图像增强	2020
MUED^[65]	8600	–	430	430个海底物体	显著性检测	2019
UOT32^[66]	24241	–	32	32个海底目标视频	目标跟踪	2019
UOT100^[67]	74042	–	104	104个海底目标视频	目标跟踪	2021

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