Measuring tailbeat frequencies of three fish species from Adaptive Resolution Imaging Sonar (ARIS) data

2021 ◽  
Author(s):  
Jani Helminen ◽  
Antóin M. O’Sullivan ◽  
Tommi Linnansaari
Keyword(s):  
Fish Species ◽  
Adaptive Resolution ◽  
Resolution Imaging ◽  
Imaging Sonar

Effectiveness of the electric fish fence as a behavioural barrier at a pumping station

2019 ◽  
Vol 70 (10) ◽  
pp. 1459 ◽  
Author(s):  
Leonhard Egg ◽  
Joachim Pander ◽  
Melanie Mueller ◽  
Juergen Geist
Keyword(s):  
Electric Fish ◽  
Pumping Station ◽  
Adaptive Resolution ◽  
Pumping Stations ◽  
Promising Solution ◽  
Resolution Imaging ◽  
Imaging Sonar ◽  
New Generation ◽  
Mean Current ◽  
High Fish

Dyke-based pumping stations have been linked with high fish mortalities during pumping events. Behavioural barriers like electric fish fences have been proposed as a promising solution to prevent entrainment of fish into pumps. In order to test the effectiveness of such barriers, the intake of a pumping station was equipped with a new generation electric fish fence while fish behaviour was observed with an adaptive resolution imaging sonar (ARIS) during non-electrified (reference) and electrified (treatment) operation modes. This study revealed the functionality of the fish fence as a behavioural barrier, with a fish turning rate of up to 72% at a mean water temperature of 4.3°C and a mean current velocity of 0.05ms–1. These field results suggest that new-generation electric fish fences may be a promising solution to reduce the effects of pumping stations on fish.

scholarly journals Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning

2022 ◽  
Vol 8 ◽  
Author(s):  
Vishnu Kandimalla ◽  
Matt Richard ◽  
Frank Smith ◽  
Jean Quirion ◽  
Luis Torgo ◽  
...  
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
High Resolution ◽  
Real Time ◽  
Fish Species ◽  
Fish Passage ◽  
High Resolution Imaging ◽  
Fish Ladder ◽  
Resolution Imaging ◽  
Imaging Sonar

The Ocean Aware project, led by Innovasea and funded through Canada's Ocean Supercluster, is developing a fish passage observation platform to monitor fish without the use of traditional tags. This will provide an alternative to standard tracking technology, such as acoustic telemetry fish tracking, which are often not appropriate for tracking at-risk fish species protected by legislation. Rather, the observation platform uses a combination of sensors including acoustic devices, visual and active sonar, and optical cameras. This will enable more in-depth scientific research and better support regulatory monitoring of at-risk fish species in fish passages or marine energy sites. Analysis of this data will require a robust and accurate method to automatically detect fish, count fish, and classify them by species in real-time using both sonar and optical cameras. To meet this need, we developed and tested an automated real-time deep learning framework combining state of the art convolutional neural networks and Kalman filters. First, we showed that an adaptation of the widely used YOLO machine learning model can accurately detect and classify eight species of fish from a public high resolution DIDSON imaging sonar dataset captured from the Ocqueoc River in Michigan, USA. Although there has been extensive research in the literature identifying particular fish such as eel vs. non-eel and seal vs. fish, to our knowledge this is the first successful application of deep learning for classifying multiple fish species with high resolution imaging sonar. Second, we integrated the Norfair object tracking framework to track and count fish using a public video dataset captured by optical cameras from the Wells Dam fish ladder on the Columbia River in Washington State, USA. Our results demonstrate that deep learning models can indeed be used to detect, classify species, and track fish using both high resolution imaging sonar and underwater video from a fish ladder. This work is a first step toward developing a fully implemented system which can accurately detect, classify and generate insights about fish in a wide variety of fish passage environments and conditions with data collected from multiple types of sensors.

scholarly journals Fish assemblages associated with artificial reefs assessed using multiple gear types in the northwest Gulf of Mexico

2020 ◽  
Vol 96 (4) ◽  
pp. 655-678
Author(s):  
Jeffrey D Plumlee ◽  
Kaylan M Dance ◽  
Michael A Dance ◽  
Jay R Rooker ◽  
Thomas C TinHan ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Fish Assemblages ◽  
Abundant Species ◽  
Red Snapper ◽  
Artificial Reefs ◽  
Adaptive Resolution ◽  
Fish Traps ◽  
Resolution Imaging ◽  
Imaging Sonar ◽  
Gray Triggerfish

Quantitative surveys of fishes associated with artificial reefs in the northwest Gulf of Mexico were conducted over a 4-yr period (2014–2017). Artificial reefs surveyed were comprised of three types: concrete structures, rig jackets, and decommissioned ships. All reefs were surveyed using vertical long line (VLL), fish traps, and Adaptive Resolution Imaging Sonar (ARIS 1800). Mean fish abundance did not significantly differ using VLL [1.7 ind set –1 (SD 2.2)] among the three reef types. However, relative abundance among all fishes collected was significantly highest on rig reefs using traps [6.2 ind soak–1 (SD 3.8)], while results from sonar surveys indicated that the mean relative fish density was highest on concrete reefs [15.3 fish frame–1 (SD 26.8)]. Red snapper (n = 792), followed by gray triggerfish (n = 130), pigfish (n = 70), tomtate (n = 69), and hardhead catfish (n = 57) were the most numerically abundant species using VLL and traps; red snapper comprised 90.7% of total catch using VLL and 43.9% using traps. Mean Brillouin's diversity (HB) was highest on ships using VLL [0.41 (SD 0.14)] and highest on rigs using traps [0.87 (SD 0.58)] compared to the lowest diversity found on concrete [VLL 0.07 (SD 0.11); traps 0.36 (SD 0.32)]. Findings from this study can be used to inform the planning of future artificial reefs and their effect on the assemblages of reef-associated fishes. Additionally, these results highlight the value of using multiple gear types to survey reef fish assemblages associated with artificial reefs.

Detecting a nearshore fish parade using the adaptive resolution imaging sonar (ARIS): An automated procedure for data analysis

2017 ◽  
Vol 191 ◽  
pp. 190-199 ◽  
Author(s):  
Suzan Shahrestani ◽  
Hongsheng Bi ◽  
Viacheslav Lyubchich ◽  
Kevin M. Boswell
Keyword(s):  
Data Analysis ◽  
Adaptive Resolution ◽  
Resolution Imaging ◽  
Imaging Sonar

Adaptive Resolution Imaging Sonar (ARIS) as a tool for marine fish identification

2021 ◽  
Vol 243 ◽  
pp. 106092
Author(s):  
Robyn E. Jones ◽  
Ross A. Griffin ◽  
Richard K.F. Unsworth
Keyword(s):  
Marine Fish ◽  
Adaptive Resolution ◽  
Resolution Imaging ◽  
Imaging Sonar

Can acoustic shadows identify fish species? A novel application of imaging sonar data

2012 ◽  
Vol 19 (4) ◽  
pp. 313-322 ◽  
Author(s):  
M. C. LANGKAU ◽  
H. BALK ◽  
M. B. SCHMIDT ◽  
J. BORCHERDING
Keyword(s):  
Fish Species ◽  
Imaging Sonar
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