Seg2Sonar: A Full-Class Sample Synthesis Method Applied to Underwater Sonar Image Target Detection, Recognition, and Segmentation Tasks

声纳 人工智能 图像分割 计算机科学 水下 计算机视觉 样品(材料) 分割 模式识别(心理学) 班级(哲学) 合成孔径声纳 地质学 海洋学 化学 色谱法
作者
Chao Huang,Jianhu Zhao,Hongmei Zhang,Yongcan Yu
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:62: 1-19 被引量:3
标识
DOI:10.1109/tgrs.2024.3363875
摘要

To overcome the challenges of limited samples, difficult acquisition, under-representation, and labeling in utilizing sonar images and deep learning for target detection, recognition, and segmentation tasks for full-class underwater targets, we propose the Seg2Sonar network based on SPADE. This network generates images through segmentation maps, thus eliminating the need for sample annotation. Additionally, we incorporate the Skip-Layer channel-wise Excitation (SLE) module into the SPADE network to enhance feature extraction ability with minimal training samples. To improve the realism of generated images, we introduce the Focal Frequency Loss (FFL) module, and propose the Elasticity loss (EL) strategy to improve the random combination capability of the network, considering the characteristics of low resolution and severe distortion of sonar images. Furthermore, we propose a weight adjustment (WA) strategy that tackles the challenge of low and unbalanced feature representation with few samples by taking into account the unbalanced distribution of features using prior information. hese four improvements enable efficient sample augmentation of sonar images with limited samples. Building upon the improved Seg2Sonar network, we propose an underwater full-class target augmentation strategy. Based on the imaging characteristics of sonar images, we classify underwater full-class targets into four categories: texture level, group level, shape level, and intensity level. We provide corresponding augmentation strategies by leveraging similar features among sonar target images or adding external radar/optical features to supplement the diversity of features. Our experimental results demonstrate the efficacy of our proposed method in achieving sample augmentation of underwater full-class targets with minimal samples (less than 10) or even zero samples. The approach achieves about 90% accuracy in detection, recognition, and segmentation for all types of targets through deep learning methods. Our findings provide a promising solution for efficient sample augmentation of underwater full-class targets with limited samples.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
ableyy发布了新的文献求助10
1秒前
活力惜寒完成签到,获得积分10
1秒前
Jasper应助魁梧的小伙子采纳,获得10
2秒前
2秒前
jiajia发布了新的文献求助10
2秒前
Natforever完成签到 ,获得积分10
3秒前
量子星尘发布了新的文献求助10
3秒前
lumen完成签到,获得积分10
4秒前
飞飞完成签到,获得积分10
5秒前
5秒前
tutu完成签到,获得积分10
5秒前
5秒前
5秒前
nijin发布了新的文献求助10
6秒前
忐忑的黄豆完成签到,获得积分10
6秒前
6秒前
可耐的发夹完成签到,获得积分10
6秒前
6秒前
小章鱼发布了新的文献求助30
7秒前
bc完成签到,获得积分10
7秒前
没有名字完成签到,获得积分10
7秒前
华仔应助王王赵采纳,获得10
8秒前
8秒前
txxxx完成签到,获得积分10
9秒前
myn1990发布了新的文献求助10
9秒前
李爱国应助今日不再蛇皇采纳,获得10
9秒前
znn发布了新的文献求助10
10秒前
XXXX发布了新的文献求助10
10秒前
池塘的小海豚完成签到,获得积分10
10秒前
小蘑菇应助哈哈哈哈采纳,获得10
11秒前
喜悦代双完成签到,获得积分10
11秒前
张小陈完成签到 ,获得积分10
11秒前
金思明发布了新的文献求助10
11秒前
llwxx发布了新的文献求助10
11秒前
只只发布了新的文献求助10
12秒前
myf完成签到 ,获得积分10
12秒前
yisu完成签到,获得积分10
12秒前
顷梦完成签到,获得积分10
13秒前
可玩性发布了新的文献求助50
13秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3974712
求助须知:如何正确求助?哪些是违规求助? 3519159
关于积分的说明 11197254
捐赠科研通 3255257
什么是DOI,文献DOI怎么找? 1797724
邀请新用户注册赠送积分活动 877130
科研通“疑难数据库(出版商)”最低求助积分说明 806132