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Citation link: https://media.suub.uni-bremen.de/handle/elib/6578

Publisher DOI: https://doi.org/10.3390/jmse9030239
 
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Generating Synthetic Sidescan Sonar Snippets Using Transfer-Learning in Generative Adversarial Networks


Authors: Steiniger, Yannik  
Kraus, Dieter  
Meisen, Tobias  
Abstract: 
The training of a deep learning model requires a large amount of data. In case of sidescan sonar images, the number of snippets from objects of interest is limited. Generative adversarial networks (GAN) have shown to be able to generate photo-realistic images. Hence, we use a GAN to augment a baseline sidescan image dataset with synthetic snippets. Although the training of a GAN with few data samples is likely to cause mode collapse, a combination of pre-training using simple simulated images and fine-tuning with real data reduces this problem. However, for sonar data, we show that this approach of transfer-learning a GAN is sensitive to the pre-training step, meaning that the vanishing of the gradients of the GAN’s discriminator becomes a critical problem. Here, we demonstrate how to overcome this problem, and thus how to apply transfer-learning to GANs for generating synthetic sidescan snippets in a more robust way. Additionally, in order to further investigate the GAN’s ability to augment a sidescan image dataset, the generated images are analyzed in the image and the frequency domain. The work helps other researchers in the field of sonar image processing to augment their dataset with additional synthetic samples.
Keywords: deep learning; generative adversarial networks; transfer-learning; sidescan sonar; synthetic sonar images
Issue Date: 2021
Publisher: MDPI
Journal/Edited collection: Journal of Marine Science and Engineering 
Start page: 239
Note: 3
Band: 9
Type: Artikel/Aufsatz
ISSN: 2077-1312
Institution: Hochschule Bremen 
Faculty: Hochschule Bremen - Fakultät 4: Elektrotechnik und Informatik 
Appears in Collections:Bibliographie HS Bremen

  

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