Fish species classification in underwater video monitoring using Convolutional Neural Networks

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Authors

Frederik Kratzert, Helmut Mader

Abstract

This report presents a case study for automatic fish species classification in underwater video monitoring of fish passes. Although the presented approach is based on the FishCam monitoring system, it can be used with any video-based monitoring system. The presented classification scheme in this study, is based on Convolutional Neural Networks that do not require the calculation of any hand-engineered image features. Instead, these networks use the raw video image as input. Additionally, this study investigates, if the classification accuracy can be increased by adding additional meta-information (date of migration and fish length) to the network. The approach is tested on a subset of 10 fish species (8099~individuals) occurring in Austrian river. On an independent test set, the presented approach achieves a classification accuracy of 93 %.

DOI

https://doi.org/10.31223/osf.io/dxwtz

Subjects

Animal Sciences, Aquaculture and Fisheries Life Sciences, Computer Sciences, Environmental Monitoring, Environmental Sciences, Life Sciences, Other Life Sciences, Physical Sciences and Mathematics, Software Engineering

Keywords

fish, Neural Networks, image classification, fish migration, fish monitoring

Dates

Published: 2018-05-15 17:07

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License

CC BY Attribution 4.0 International

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