Annotated Video Footage for Automated Identification and Counting of Fish in Unconstrained Seagrass Habitats
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., 2015; Weinstein, 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., 2015; Villon et al., 2016). As the use of camera systems for monitoring fish abundances is common practice in conservation ecology (Gilby et al., 2017; Whitmarsh et al., 2017; Langlois 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.