M.S. Bewley, B. Douillard, N. Nourani-Vatani, A. Friedman, O. Pizarro, S.B. Williams
Australasian Conference of Robotics and Automation, Wellington, New Zealand
This paper presents an experimental study of automated species detection systems suitable for use with Autonomous Underwater Vehicle (AUV) data. The automated detection systems presented in this paper use supervised learning; a support vector machine and local image features are used to predict the presence or ab- sence of Ecklonia Radiata (kelp) in sea floor images. A comparison study was done using a variety of descriptors (such as local binary patterns and principal component analysis) and image scales. The performance was tested on a large data set of images from 14 AUV missions, with more than 60,000 expert labelled points. The best performing model was then analysed in greater detail, to estimate performance on generalising to unseen AUV missions, and char- acterise errors that may impact the utility of the species detection system for marine scientists.