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

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 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.

Authors:
 [1];  [1]; ORCiD logo [1];  [2];  [1]; ORCiD logo [1]
  1. BATTELLE (PACIFIC NW LAB)
  2. Electric Power Research Institute
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1598802
Report Number(s):
PNNL-SA-144397
Journal ID: ISSN 2572-6862
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS-53) January 6-10, 2020, Maui, HI
Country of Publication:
United States
Language:
English

Citation Formats

Yin, Tianzhixi, Zang, Xiaoqin, Hou, Zhangshuan, Jacobson, Paul, Mueller, Robert P., and Deng, Zhiqun. Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network. United States: N. p., 2020. Web. doi:10.24251/HICSS.2020.116.
Yin, Tianzhixi, Zang, Xiaoqin, Hou, Zhangshuan, Jacobson, Paul, Mueller, Robert P., & Deng, Zhiqun. Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network. United States. doi:10.24251/HICSS.2020.116.
Yin, Tianzhixi, Zang, Xiaoqin, Hou, Zhangshuan, Jacobson, Paul, Mueller, Robert P., and Deng, Zhiqun. Tue . "Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network". United States. doi:10.24251/HICSS.2020.116.
@article{osti_1598802,
title = {Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network},
author = {Yin, Tianzhixi and Zang, Xiaoqin and Hou, Zhangshuan and Jacobson, Paul and Mueller, Robert P. and Deng, Zhiqun},
abstractNote = {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 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.},
doi = {10.24251/HICSS.2020.116},
journal = {},
issn = {2572-6862},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {1}
}

Conference:
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