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.authorYin, Tianzhixi
dc.contributor.authorZang, Xiaoqin
dc.contributor.authorHou, Z. Jason
dc.contributor.authorJacobson, Paul
dc.contributor.authorMueller, Robert
dc.contributor.authorDeng, Zhiqun
dc.date.accessioned2020-01-04T07:20:01Z
dc.date.available2020-01-04T07:20:01Z
dc.date.issued2020-01-07
dc.description.abstractAn 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.extent8 pages
dc.identifier.doi10.24251/HICSS.2020.116
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63855
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAnalytics and Decision Support for Green IS and Sustainability Applications
dc.subjectconvolutional neural network
dc.subjecteel detection
dc.subjectsonar
dc.subjecttransfer learning
dc.titleBridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network
dc.typeConference Paper
dc.type.dcmiText

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