PNNL

Abstract

The effects of energy devices on fish populations are not well-understood, and studying the interactions of fish with tidal and instream turbines is challenging. To address this problem, we have evaluated algorithms to automatically detect fish in underwater video and propose a semi-automated method for ocean and river energy device ecological monitoring. The key contributions of this work are the demonstration of a background subtraction algorithm (ViBE) that detected 87% of human-identified fish events and is suitable for use in a real-time system to reduce data volume, and the demonstration of a statistical model to classify detections as fish or not fish that achieved a correct classification rate of 85% overall and 92% for detections larger than 5 pixels. Specific recommendations for underwater video acquisition to better facilitate automated processing are given. The recommendations will help energy developers put effective monitoring systems in place, and could lead to a standard approach that simplifies the monitoring effort and advances the scientific understanding of the ecological impacts of ocean and river energy devices.