Recent developments in low-cost autonomous underwater vehicles (AUVs), stationary camera arrays, and towed vehicles have made it possible for fishery scientists to begin using optical data streams (e.g. still and video imagery) to generate species-specific, size-structured abundance estimates for different species of marine organisms. Increasingly, NOAA Fisheries and other agencies are employing camera-based surveys to estimate size-structured abundance for key stocks. While there are many benefits to optical surveys, including reduced inter-observer error as well as the ability to audit the observations and generate high sample sizes with reduced personnel and days at sea, the volume of optical data generated quickly exceeds the capabilities of human analysis.
Automated image processing methods have been developed and utilized in the human surveillance, biomedical, and defense domains for some time (LeCun et al. 2015; Szeliski 2010) and there are currently many open-source computer vision libraries and packages available on the internet. In the marine science environment, however, computer vision has yet to reach its full potential. Techniques for automated detection, identification, measurement, tracking, and counting fish in underwater optical data streams do exist (Chuang et al. 2014a, 2014b, 2013, 2011; Williams et al. 2016), however, few of these systems are fully automated, with all of the functions required to produce highly successful and accurate results.
Marine scientists rarely possess formal programming and development experience. Hence, existing solutions typically exist as one-off, localized applications, specific to particular analysis tasks. As such, they are generally non-transferrable as functional applications with utility across the domain. Consequently, with few exceptions (Huang et al. 2012; Williams et al. 2012; Chuang et al. 2014b; Chuang et al. 2014a; National Research Council 2014; Fisher et al. 2016; and Williams et al. 2016) there has been little operational use of automated analysis within the marine science community.
In response to this need, in 2011, the NOAA Fisheries OST initiated a Strategic Initiative on Automated Image Analysis (SI). The mission of this SI was to develop guidelines, set priorities, and fund projects to develop broad-scale, standardized, and efficient automated tools for the analysis of optical data for use in stock assessment. The goal is to create an end-to-end open source software toolkit that allows for the automated analysis of optical data streams and in turn provide fishery-independent abundance estimates for use in stock assessment.