Reef fish identification through image records using artificial intelligence and convolutional neural network system

Abstract

Coral reef environments show great diversity and abundance of species that represent important biological resources for obtaining food and medicines, in addition to acting as bioindicators of the quality of reef environments, serving as a parameter to diagnose environmental impacts and, simultaneously, to evaluate the availability and use of fishing stocks and biological resources in such environments. Monitoring such  ecosystems arises from the urgent need to better understand the natural geographic and environmental variability of these systems, as well as their main drivers of change, to inform and promote more effective management of such environments. With the growing need to monitor communities of reef organisms, particularly fish, the creation of a digital analysis method would make a great contribution to the studies of reef fish. To fill this knowledge gap, our work evaluates the possibility of using an artificial intelligence mechanism using convolutional neural networks (CNN) to identify and count reef fish in image records, to optimize digital analysis of videos and/or photos in reef environments for fish ecology studies. The accuracy of the CNN identification method was effective, although it still has challenges to improve, such as: the need for a preliminary survey of reef fish species existing in the target area to be monitored, with the capture of good resolution images in different positions space for each species; production of an image bank with a large number of diverse images for each species (minimum of 200 images per species) and; To detect fish in videos, fast computers with a dedicated GPU card are needed, especially if the videos are high or ultra-resolution and have a large number of fps.