Semiautomated Analysis of Data from an Imaging Sonar for Fish Counting, Sizing, and Tracking in a Post-Processing Application
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초록
Dual frequency identification sonar (DIDSON) is an imaging sonar that has been used for numerous fisheries investigations in a diverse range of freshwater and marine environments. The main purpose of DIDSON is fish counting, fish sizing, and fish behavioral studies. DIDSON records video-quality data, so processing power for handling the vast amount of data with high speed is a priority. Therefore, a semiautomated analysis of DIDSON data for fish counting, sizing, and fish behavior in Echoview (fisheries acoustic data analysis software) was accomplished using testing data collected on the Rakaia River, New Zealand. Using this data, the methods and algorithms for background noise subtraction, image smoothing, target (fish) detection, and conversion to single targets were precisely illustrated. Verification by visualization identified the resulting targets. As a result, not only fish counts but also fish sizing information such as length, thickness, perimeter, compactness, and orientation were obtained. The alpha-beta fish tracking algorithm was employed to extract the speed, change in depth, and the distributed depth relating to fish behavior. Tail-beat pattern was depicted using the maximum intensity of all beams. This methodology can be used as a template and applied to data from BlueView two-dimensional imaging sonar.
Abstract
Dual frequency identification sonar (DIDSON) is an imaging sonar that has been used for numerous fisheries investigations in a diverse range of freshwater and marine environments. The main purpose of DIDSON is fish counting, fish sizing, and fish behavioral studies. DIDSON records video-quality data, so processing power for handling the vast amount of data with high speed is a priority. Therefore, a semiautomated analysis of DIDSON data for fish counting, sizing, and fish behavior in Echoview (fisheries acoustic data analysis software) was accomplished using testing data collected on the Rakaia River, New Zealand. Using this data, the methods and algorithms for background noise subtraction, image smoothing, target (fish) detection, and conversion to single targets were precisely illustrated. Verification by visualization identified the resulting targets. As a result, not only fish counts but also fish sizing information such as length, thickness, perimeter, compactness, and orientation were obtained. The alpha-beta fish tracking algorithm was employed to extract the speed, change in depth, and the distributed depth relating to fish behavior. Tail-beat pattern was depicted using the maximum intensity of all beams. This methodology can be used as a template and applied to data from BlueView two-dimensional imaging sonar.
참고문헌 (17)
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