Optimizing video sampling for juvenile fish surveys: using deep learning and evaluation of assumptions to produce critical fisheries parameters

The limitations imposed by traditional sampling methods have restricted the acquisi- tion of data on key fisheries parameters. This is particularly the case for juveniles because most traditional gear explicitly avoids the capture of juveniles, and the juveniles of many species use habitats in which traditional gear is ineffective. The increasing availability and sophistication of Remote Underwater Video Techniques (RUVs) such as Baited Remote Underwater Video, Unbaited Remote Underwater Video and Remotely Operated Underwater Vehicles offer the opportunity of over- coming some of the key limitations of more traditional approaches. However, RUV techniques come with their own set of limitations that need to be addressed before they can fully realize their potential to shed new light on the early life history of fish. We evaluate key strengths and limitations of RUV techniques, and how these can be overcome, in particular by employing bespoke computer vision Artificial Intelligence approaches, such as Deep Learning in its Convolutional Neural Networks instantia- tion. In addition, we investigate residual issues that remain to be solved despite the advances made possible by new technology, and the role of explicitly identifying and evaluating key residual assumptions