Abstract#R##N#This paper presents a system capable of autonomous surveillance and analysis of coral reef ecosystems using natural lighting. We describe our strategy to safely and effectively deploy a small marine robot to inspect a reef using its digital cameras. Image analysis using a (RBF-SVM) radial basis function-support vector machines in combination with (LBP) local binary pattern, Gabor and Hue descriptors developed in this work are able to analyze the resulting image data automatically and reliably by learning from the annotations of expert marine biologists. Our primary evaluation is performed on a novel coral data set that we collected during a series of robotic ocean deployments, the MRL Coral Identification Challenge. We have also applied our algorithms to a data set of coral imagery previously published by other researchers. Our algorithms recognize coral images in our own challenging data with 88.9% accuracy, while being sufficiently efficient to run online on our vehicle. This demonstrates the feasibility of such a system for practical use for the preservation of this crucial ecological resource.
['Travis Manderson', 'Jimmy Li', 'Natasha Dudek', 'David Meger', 'Gregory Dudek']
Robotic Coral Reef Health Assessment Using Automated Image Analysis