Towards autonomous robotic coral reef health assessment

Abstract

This paper addresses the automated analysis of coral in shallow reef environments up to 90 ft deep. During a series of robotic ocean deployments, we have collected a data set of coral and non-coral imagery from four distinct reef locations. The data has been annotated by an experienced biologist and presented as a representative challenge for visual understanding techniques. We describe baseline techniques using texture and color features combined with classifiers for two vision sub-tasks: live coral image classification and live coral semantic segmentation. The results of these methods demonstrate both the feasibility of the task as well as the remaining challenges that must be addressed through the development of more sophisticated techniques in the future.