Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data
| Autor: | Changkyu Choi, Michael Kampffmeyer, Nils Olav Handegard, Arnt-Børre Salberg, Robert Jenssen |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
VDP::Technology: 500::Marine technology: 580
Datasyn Computer Vision Datasyn / Computer Vision Marinteknologi / Marine Technology Ocean Engineering VDP::Matematikk og naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Algoritmer og beregnbarhetsteori: 422 VDP::Teknologi: 500::Marin teknologi: 580 Marine acoustic data analysis / Marine acoustic data analysis VDP::Algoritmer og beregnbarhetsteori: 422 Semi-supervised deep learning VDP::Mathematics and natural scienses: 400::Mathematics: 410::Statistics: 412 Electrical and Electronic Engineering VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 Artificial Neural Networks VDP::Algorithms and computability theory: 422 Nevrale nettverk Mechanical Engineering VDP::Technology: 500::Information and communication technology: 550 Deep learning Marine Technology Semi-supervised deep learning / Semi-supervised deep learning Artificial Neural Networks / Artificial Neural Networks Deep learning / Deep learning Marine acoustic data analysis VDP::Landbruks- og fiskerifag: 900::Fiskerifag: 920 Nevrale nettverk / Neural networks VDP::Mathematics and natural scienses: 400::Information and communication science: 420::Algorithms and computability theory: 422 Marinteknologi VDP::Agriculture and fisheries science: 900::Fisheries science: 920 VDP::Matematikk og naturvitenskap: 400::Matematikk: 410::Statistikk: 412 Neural networks |
| Zdroj: | IEEE Journal of Oceanic Engineering |
| Popis: | Multi-frequency echosounder data can provide a broad understanding of the underwater environment in a non-invasive 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 the 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 semi-supervised semantic segmentation method, all embodied into a single end-to-end trainable convolutional neural networks 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 percent of the annotated data on which the supervised method is trained, by leveraging unannotated data. The code is available at https://github.com/SFI-Visual-Intelligence/PredKlus-semisup-segmentation. Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data |
| Databáze: | OpenAIRE |
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