Autoencoder-Combined Generative Adversarial Networks for Synthetic Image Data Generation and Detection of Jellyfish Swarm

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Image-based sensing of jellyfish is important as they can cause great damage to the fisheries and seaside facilities and need to be properly controlled. In this paper, we present a deep-learning-based technique to generate a synthetic image of the jellyfish easily with autoencoder-combined generative adversarial networks. The proposed system can easily generate simple images with a smaller number of data sets compared with other generative networks. The generated output showed high similarity with the real-image data set. The application using a fully convolutional network and regression network to estimate the size of the jellyfish swarm was also demonstrated, and showed high accuracy during the estimation test.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-12
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.6, no.1, pp.54207 - 54214

ISSN
2169-3536
DOI
10.1109/ACCESS.2018.2872025
URI
http://hdl.handle.net/10203/246516
Appears in Collection
EE-Journal Papers(저널논문)
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