声呐图像智能感知算法综述

焦文沛; 李杰; 张春燕; 谢广明; 肖文栋; 张建磊

doi:10.11993/j.issn.2096-3920.2024-0127

声呐图像智能感知算法综述

doi: 10.11993/j.issn.2096-3920.2024-0127

1.
南开大学人工智能学院, 天津, 300350
2.
北京大学工学院, 北京, 100071
3.
北京科技大学自动化学院, 北京, 100083

基金项目: 国家自然科学基金项目(62473211, 62073174, 62073175).

详细信息

作者简介:
焦文沛(2000-), 男, 在读硕士, 主要研究方向为模式识别及声呐图像处理

通讯作者:
张建磊(1981-), 男, 博士, 教授, 主要研究方向为深度学习、强化学习及声呐图像处理.

中图分类号: TJ630.34; U666.74
计量
- 文章访问数: 112
- HTML全文浏览量: 39
- PDF下载量: 35
- 被引次数: 0
出版历程
- 收稿日期: 2024-08-09
- 修回日期: 2024-09-29
- 录用日期: 2024-10-21
- 网络出版日期: 2025-05-07

Intelligent Perception Algorithms for Sonar Images: A Review

1.
College of Artificial Intelligence, Nankai University, Tianjin 300350, China
2.
College of Engineering, Peking University, Beijing 100071, China
3.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

摘要

摘要: 声呐图像智能感知算法在海洋探测与水下救援中具有至关重要的作用。近年来, 深度学习技术在声呐图像智能感知任务中取得了显著进展。文中对该领域进行了全面梳理, 从声呐图像数据集与数据增强技术、经典的声呐图像处理算法以及基于深度学习的声呐图像处理方法3个方面进行探讨。首先, 归纳了不同任务的开源数据集与常用的数据增强技术, 为后续研究提供数据支撑; 其次, 系统回顾了从经典算法到基于深度学习的先进算法在不同任务中的应用与发展现状, 旨在为研究者提供系统的领域概览; 最后, 展望了未来的研究方向, 指出可通过获取更大规模的声呐图像数据、设计更强健的算法以及开发更适用于真实水下场景的任务设置等方面, 进一步提升声呐图像的解译能力。
- 声呐图像 /
- 智能感知 /
- 海洋探测 /
- 深度学习
Abstract: Intelligent perception algorithms for sonar images are vital in ocean exploration and underwater rescue. In recent years, deep learning has achieved remarkable advancements in intelligent perception tasks related to sonar images. This paper provided a comprehensive review of the field, focusing on sonar image datasets and data augmentation techniques, classical sonar image processing algorithms, and deep learning-based sonar image processing methods. By summarizing open-source datasets and commonly used data augmentation techniques, the paper provided data support for future research efforts. Additionally, this paper systematically analyzed the application and evolution of both classical and deep learning-based algorithms across various tasks, offering researchers an overview of the current state of the field. Finally, based on the research progress, the paper predicted future research directions. It was pointed out that the interpretation ability of sonar images could be further improved by obtaining larger-scale sonar image data, designing more robust algorithms, and developing task settings that are more suitable for real-world underwater environments.
- sonar image /
- intelligent perception /
- ocean exploration /
- deep learning

HTML全文

图 1 声呐图像智能感知整体流程

Figure 1. The whole process of sonar image intelligent perception

下载: 全尺寸图片幻灯片

图 2 基于深度CNN模型的迁移学习方法示意图

Figure 2. Schematic diagram of transfer learning method based on deep CNN model

下载: 全尺寸图片幻灯片

表 1 现有声呐图像分类数据集信息汇总表

Table 1. Summary of existing sonar image classification datasets information

数据集	类别	数量	总数	种类	不平衡因子
Seabed Objects-KLSG	飞机	62	447	侧扫	6.21
Seabed Objects-KLSG	沉船	385	447	侧扫	6.21
FLSMDD	瓶子	449	2 364	前视	6.91
	罐子	367
	链条	226
	饮料瓶	349
	钩子	133
	螺旋桨	137
	洗发水瓶	99
	直立状瓶子	65
	轮胎	331
	阀门	208
NKSID	大螺旋桨	203	2 617	前视	47.55
	圆柱体	288
	渔网	20
	浮球	951
	钢管	112
	小螺旋桨	94
	软管	115
	轮胎	834

下载: 导出CSV

表 2 现有声呐图像目标检测数据集信息汇总表

Table 2. Summary of existing sonar image target detection datasets information

数据集	类别	数量	总数	种类
FLS-DD	飞机	1 105	3 752	前视
	沉船	884
	溺水者	1 363
SCTD	飞机	57	357	侧扫
	沉船	34
	溺水者	266
UATD	立方体	2 987	9 200	前视
	球体	3 463
	圆柱体	657
	溺水者	1 434
	轮胎	1 368
	圆形网箱	860
	方形网箱	1 318
	金属桶	487
	飞机	1 065
	水下航行器	1 000

下载: 导出CSV

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