.png){kind=icon}
返回
Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data
.png){kind=icon}
OA
AI
DOI：10.1109/JOE.2022.3226214.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multifrequency echosounder data can provide a broad understanding of the underwater environment in a noninvasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. Semantic segmentation, a deep learning-based analysis method predicting the class attribute of each acoustic intensity, has recently been in the spotlight of the fisheries and aquatic industry since its result can be used to estimate the abundance of marine organisms. However, a fundamental problem with current methods is the massive reliance on the availability of large amounts of annotated training data, which can only be acquired through expensive handcrafted annotation processes, making such approaches unrealistic in practice. As a solution to this challenge, we propose a novel approach, where we leverage a small amount of annotated data (supervised deep learning) and a large amount of readily available unannotated data (unsupervised learning), yielding a new data-efficient and accurate semisupervised semantic segmentation method, all embodied into a single end-to-end trainable convolutional neural network architecture. Our method is evaluated on representative data from a sandeel survey in the North Sea conducted by the Norwegian Institute of Marine Research. The rigorous experiments validate that our method achieves comparable results utilizing only 40% of the annotated data on which the supervised method is trained, by leveraging unannotated data.
Keyword:
Convolutional neural networks
Linear programming
Backscatter
Annotations
Training data
Task analysis
Semantics
Acoustic target classification
convolutional neural networks
deep clustering
deep learning
marine acoustics
multifrequency echosounder data
semisupervised semantic segmentation
期刊
IF:
5.3
论文数:
2.7K
被引数:
7.4K
机构
引用论文
Types of Parent Verbal Responsiveness That Predict Language in Young Children With Autism Spectrum Disorder预测自闭症谱系障碍幼儿语言的父母言语反应类型
OAAI
OAAI分享收藏Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps卷积神经网络用于结直肠息肉语义分割的不确定性和可解释性.png)
MEDICAL IMAGE ANALYSISOAAI
MEDICAL IMAGE ANALYSISIF11.8
OAAI分享收藏Differentiation of two swim bladdered fish species using next generation wideband hydroacousticsSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏
查看更多内容