Journal Article

Annotated Video Footage for Automated Identification and Counting of Fish in Unconstrained Seagrass Habitats

Lopez-Marcano, Sebastian, Ditria, Ellen M., Connolly, Rod M., Jinks, Eric L.

2021

Overview

Automated monitoring using deep learning can reduce labor costs and increase efficiency and has been shown to be equally or more accurate than humans at processing data (Torney et al., 2019; Ditria et al., 2020a). [...]the expansion of deep learning techniques in the last few years in marine science call for higher volumes of data for training than traditional machine learning methods. [...]there is a need for accessible, quality annotated datasets for deep learning models to further the progress of applying these techniques in ecology. The contributions of this dataset include: (1) a comprehensive dataset of ecologically important fish species that captures the complexity of backgrounds observed in unconstrained seagrass ecosystems to form a robust and flexible model; (2) a variety of modalities for rapid and flexible testing or comparison of different frameworks, and (3) unaltered imagery for investigation of possible data augmentation and performance enhancement using pre- and post-processing techniques. Dataset To continue the development of automated tools for fish monitoring, we report a dataset “Annotated videos of luderick from estuaries in southeast Queensland, Australia” which was used to train a deep learning algorithm for automated species identification and abundance counts presented in Ditria et al.

• Publisher: Frontiers Research Foundation,Frontiers Media S.A
• Subject: Algorithms / annotated dataset / Annotations / Aquatic ecosystems / automated monitoring / Automation / Cameras / Datasets / Deep learning / deep learning—CNN / Ecology / Estuaries / Fish / Identification / Imagery / Labour costs / Learning algorithms / Localization / Machine learning / Marine sciences / Sea grasses / seagrass / Species identification / Training / Underwater habitats