@article{f9b04aa6ffdd4228b8171af37fefa3e0,
title = "Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning",
author = "Vishnu Kandimalla and Matt Richard and Frank Smith and Jean Quirion and Luis Torgo and Chris Whidden",
note = "Funding Information: To overcome these challenges, we applied deep neural network algorithms to video data from both cameras and imaging sonar to develop automated fish detection and classification techniques. The Ocean Aware project, funded by Canada{\textquoteright}s Ocean Supercluster and led by Innovasea, plans to build, and commercialize world-class solutions for tracking fish health and innovative approaches to assessment. The main aim of this project is to get a clearer understanding of the nature and movement of fish species in realtime to inform regulation and mitigation of human impacts on at risk fish. As part of this Ocean Aware project, our research focuses on acoustic and video camera data processing techniques that aid in the detection, classification, and tracking of fish. Funding Information: We wish to thank Innovasea staff Aubrey Ingraham, Jeff Garagan, and Tim Hatt for labeling the larger subset of the Ocequeoc River DIDSON dataset. We also want to acknowledge the support of DeepSense staff Jason Newport, Lu Yang, Geetika Bhatia and Mahbubur Rahman who contributed suggestions and computing support. This work is based on VK's thesis Kandimalla (2021). Publisher Copyright: Copyright {\textcopyright} 2022 Kandimalla, Richard, Smith, Quirion, Torgo and Whidden.",
year = "2022",
month = jan,
day = "13",
doi = "10.3389/fmars.2021.823173",
language = "English",
volume = "8",
journal = "Frontiers in Marine Science",
issn = "2296-7745",
publisher = "Frontiers Media S. A.",
}
