Abstract

Techniques for marine monitoring have been greatly evolved over the past decades, making the acquisition of environmental data safer, more reliable and more efficient. On the other hand, the marine renewable energy sector has introduced dissimilar ways of exploring the oceans. Marine energy is mostly harvested in murky and high energetic places where conventional data acquisition techniques are impractical. This new frontier on marine operations brings the need for finding new techniques for environmental data acquisition, processing and analysis. Modern sonar systems, operating at high frequencies, can acquire detailed images of the underwater environment. Variables such as occurrence, size, class and behavior of a variety of aquatic species of fish, birds, and mammals that coexist within marine energy sites can be monitored using imaging sonar systems. Although sonar images can provide high levels of detail, in most of the cases they are still difficult to decipher. In order to facilitate the classification of targets using sonar images, this study introduces a framework of extracting visual features of marine animals that would serve as unique signatures. The acoustic visibility measure (AVM) is here introduced as technique of identification and classification of targets by comparing the observed size with a standard value. This information can be used to instruct algorithms and protocols in order to automate the identification and classification of underwater targets using imaging sonar systems. Using image processing algorithms embedded in Proviwer4 and FIJI software, this study found that acoustic images can be effectively used to classify cod, harbour and grey seals, and orcas through their size, shape and swimming behavior. The sonar images showed that cod occurred as bright, 0.9 m long, ellipsoidal targets shoaling in groups. Harbour seals occurred as bright torpedo-like fast moving targets, whereas grey seals occurred as bulky-ellipsoidal targets with serpentine movements. Orca or larger marine mammals occurred with relatively low visibility on the acoustic images compared to their body size, which measured between 4 m and 7 m. This framework provide a new window of performing qualitative and quantitative observations of underwater targets, and with further improvements, this method can be useful for environmental studies within marine renewable energy farms and for other purposes.

Highlights

  • Given the high potential that clean harvesting technologies have, marine renewables soon may be integrated into the energy mix

  • The objective of the present study is to propose an easy target detection and classification framework that can facilitate the interpretation of acoustic images of fish and marine mammals

  • Two Uppsala University (UU)-wave energy converters (WECs) were covered within the sonar field of view (FOV), WEC A was located at 30 m of range with a yaw angle of 58◦, and WEC B was located at 50 m of range with a yaw angle of 52◦

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Summary

Introduction

Given the high potential that clean harvesting technologies have, marine renewables soon may be integrated into the energy mix. The exploration of renewable energy in the marine environment takes place where the physical conditions are dominated by high seas, strong winds, deep and murky waters [4,5]. Scientifically approved methods of observing marine mammals and fish still involve the direct human observation, the use of cameras, tags, echo-sounders, capture and diving [6,7]. These conventional techniques require substantial resources while incurring high risks and costs. These limitations can be addressed by utilizing alternative technologies such as high frequency sonar systems [8,9]

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