Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network

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dc.contributor.author Yin, Tianzhixi
dc.contributor.author Zang, Xiaoqin
dc.contributor.author Hou, Z. Jason
dc.contributor.author Jacobson, Paul
dc.contributor.author Mueller, Robert
dc.contributor.author Deng, Zhiqun
dc.date.accessioned 2020-01-04T07:20:01Z
dc.date.available 2020-01-04T07:20:01Z
dc.date.issued 2020-01-07
dc.description.abstract An automatic system that utilizes data analytics and machine learning to identify adult American eel in data obtained by imaging sonars is created in this study. Wavelet transform has been applied to de-noise the ARIS sonar data and a convolutional neural network model has been built to classify eels and non-eel objects. Because of the unbalanced amounts of data in laboratory and field experiments, a transfer learning strategy is implemented to fine-tune the convolutional neural network model so that it performs well for both the laboratory and field data. The proposed system can provide important information to develop mitigation strategies for safe passage of out-migrating eels at hydroelectric facilities.
dc.format.extent 8 pages
dc.identifier.doi 10.24251/HICSS.2020.116
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63855
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Analytics and Decision Support for Green IS and Sustainability Applications
dc.subject convolutional neural network
dc.subject eel detection
dc.subject sonar
dc.subject transfer learning
dc.title Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network
dc.type Conference Paper
dc.type.dcmi Text
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