A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis.

ABSTRACT: Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestrial camera trapping, have not been successfully applied in marine systems due to limitations of the aquatic environment. Here, we develop methodology for a system of low-cost, long-term camera traps (Dispersed Environment Aquatic Cameras), deployable over large spatial scales in remote marine environments. We use machine learning to classify the large volume of images collected by the cameras. We present a case study of these combined techniques' use by addressing fish movement and feeding behav

SUBMITTER: Bilodeau SM 

PROVIDER: S-EPMC8809566 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature