Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robust principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.
Automatic optical detection and classification of marine animals around MHK converters using machine vision
Title: Automatic optical detection and classification of marine animals around MHK converters using machine vision
January 15, 2018
Document Number: DOE-UW-0006785
Publisher: DOE EERE – Wind & Water Power Program
Brunton, S. (2018). Automatic optical detection and classification of marine animals around MHK converters using machine vision. Report by University of Washington. pp 26.