MACHINE LEARNING FOR BENTHIC TAXON IDENTIFICATION

Where seabed substrate is rocky, comprising of bedrock, boulders and cobbles, sampling and analysis of benthic ecosystems often relies on still images and video from underwater cameras. Processing benthic imagery to generate ecosystem information typically involves manual interpretation and annotation, which is a time consuming and expensive process and prone to human errors and biases. Machine learning can step in here to assist, if not fully replace manual annotation. Here, we develop an object detection model using Faster R-CNN to identify various epibenthic species from a high energy marine site with a rocky substrate. The model achieves an overall F1-score of 66.28\% across 7 different benthic species. The work is a significant step in identifying various learnings and challenges associated with data in non-ideal conditions and we suggest methods to further improve the existing framework.