Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network
Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network
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Date
2020-01-07
Authors
Yin, Tianzhixi
Zang, Xiaoqin
Hou, Z. Jason
Jacobson, Paul
Mueller, Robert
Deng, Zhiqun
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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.
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Keywords
Analytics and Decision Support for Green IS and Sustainability Applications,
convolutional neural network,
eel detection,
sonar,
transfer learning
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8 pages
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Proceedings of the 53rd Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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