Efficiency and accuracy assessment of an automated image classifier for commercially important fish species

Experts that analyze video collected from underwater optical systems can routinely provide accurate species counts. However, the high opportunity costs of expert time have entailed delays in processing large amounts of video data. Artificial Intelligence (AI) has the potential to reduce these delays and costs. To establish a baseline for this potential, we conducted a formative evaluation of a model used to count fish by species, run in the Visual and Image Analytics for Multiple Environments (VIAME) software.