DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Kyukwang | ko |
dc.contributor.author | Myung, Hyun | ko |
dc.date.accessioned | 2018-11-12T04:48:41Z | - |
dc.date.available | 2018-11-12T04:48:41Z | - |
dc.date.created | 2018-11-05 | - |
dc.date.created | 2018-11-05 | - |
dc.date.created | 2018-11-05 | - |
dc.date.created | 2018-11-05 | - |
dc.date.issued | 2018-12 | - |
dc.identifier.citation | IEEE ACCESS, v.6, no.1, pp.54207 - 54214 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10203/246516 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Autoencoder-Combined Generative Adversarial Networks for Synthetic Image Data Generation and Detection of Jellyfish Swarm | - |
dc.type | Article | - |
dc.identifier.wosid | 000448013800001 | - |
dc.identifier.scopusid | 2-s2.0-85054627843 | - |
dc.type.rims | ART | - |
dc.citation.volume | 6 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 54207 | - |
dc.citation.endingpage | 54214 | - |
dc.citation.publicationname | IEEE ACCESS | - |
dc.identifier.doi | 10.1109/ACCESS.2018.2872025 | - |
dc.contributor.localauthor | Myung, Hyun | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Autoencoder | - |
dc.subject.keywordAuthor | generative adversarial networks | - |
dc.subject.keywordAuthor | jellyfish swarm | - |
dc.subject.keywordAuthor | fully convolutional network | - |
dc.subject.keywordAuthor | regression | - |
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