Main content
Fish species classification in underwater video monitoring using Convolutional Neural Networks
- Frederik Kratzert
- Helmut Mader
Date created: | Last Updated:
: DOI | ARK
Creating DOI. Please wait...
Category: Project
Description: 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 %.
Start managing your projects on the OSF today.
Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.