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

Last modified: 
April 24, 2021 - 10:02pm
Type: Journal Article
Year of publication: 2021
Date published: 03/2021
Authors: Ellen Ditria, Rod Connolly, Eric Jinks, Sebastian Lopez-Marcano
Journal title: Frontiers in Marine Science
Volume: 8

Technological advances are improving the collection, processing and analysis of ecological data. One of these technologies that has been adopted in recent studies by ecologists is computer vision (CV). CV is a rapidly developing area of machine learning that aims to infer image content at the same level humans can by extracting information from pixels (LeCun et al., 2015Weinstein, 2018). CV in ecology has gained much attention as it can quickly and accurately process image from remote video imagery while allowing scientists to monitor both individuals and populations at unprecedented spatial and temporal scales. Automated analysis of imagery through CV has also become more accurate and streamlined with the implementation of deep learning (a subset of machine learning) models that have improved the capacity to processes raw images compared to traditional machine learning methods (LeCun et al., 2015Villon et al., 2016). As the use of camera systems for monitoring fish abundances is common practice in conservation ecology (Gilby et al., 2017Whitmarsh et al., 2017Langlois et al., 2020), deep learning allows for the automated processing of big data from video or images, a step which usually creates a bottleneck when these data must be analyzed manually.

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