scholarly journals Fish behavior and abundance monitoring near a floating surface collector in North Fork Reservoir, Clackamas River, Oregon, using multi-beam acoustic imaging sonar

2018 ◽  
Author(s):  
Collin D. Smith ◽  
John M. Plumb ◽  
Noah S. Adams
Keyword(s):  
Acoustic Imaging ◽  
Fish Behavior ◽  
North Fork ◽  
Imaging Sonar ◽  
Floating Surface

scholarly journals Observations of the volume flux of a seafloor hydrothermal plume using an acoustic imaging sonar

2013 ◽  
Vol 14 (7) ◽  
pp. 2369-2382 ◽  
Author(s):  
G. Xu ◽  
D. R. Jackson ◽  
K. G. Bemis ◽  
P. A. Rona
Keyword(s):  
Acoustic Imaging ◽  
Volume Flux ◽  
Hydrothermal Plume ◽  
Imaging Sonar

Validation of a high-resolution acoustic imaging sonar method by estimating the biomass of submerged plants in shallow water

2018 ◽  
Vol 46 ◽  
pp. 179-184 ◽  
Author(s):  
Katsunori Mizuno ◽  
Akira Asada ◽  
Syuhei Ban ◽  
Yoshitoshi Uehara ◽  
Takuya Ishida ◽  
...  
Keyword(s):  
High Resolution ◽  
Shallow Water ◽  
Acoustic Imaging ◽  
Submerged Plants ◽  
Imaging Sonar

Assessing the biological process of Hydrilla verticillata predation in a eutrophic pond using high-resolution acoustic imaging sonar

Limnology ◽  
2015 ◽  
Vol 17 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Katsunori Mizuno ◽  
Kazuki Abukawa ◽  
Toru Kashima ◽  
Akira Asada ◽  
Yasufumi Fujimoto ◽  
...  
Keyword(s):  
High Resolution ◽  
Biological Process ◽  
Hydrilla Verticillata ◽  
Acoustic Imaging ◽  
Imaging Sonar

scholarly journals Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7625
Author(s):  
Chin-Chun Chang ◽  
Yen-Po Wang ◽  
Shyi-Chyi Cheng
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Semantic Segmentation ◽  
Fish Behavior ◽  
Fish Farm ◽  
Fish Farms ◽  
Feature Maps ◽  
Sonar Systems ◽  
Sonar Images ◽  
Imaging Sonar

Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar images acquired from different shallow waters can be quite different in the contrast between fish and the background. That difference can make Mask R-CNN trained on examples collected from one fish farm ineffective to fish segmentation for the other fish farms. In this paper, a preprocessing convolutional neural network (PreCNN) is proposed to provide “standardized” feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. PreCNN aims at decoupling learning of fish instances from learning of fish-cultured environments. PreCNN is a semantic segmentation network and integrated with conditional random fields. PreCNN can utilize successive sonar images and can be trained by semi-supervised learning to make use of unlabeled information. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective.

Quantification of whooper swan damage to lotus habitats using high-resolution acoustic imaging sonar in Lake Izunuma, Japan

2013 ◽  
Vol 110 ◽  
pp. 48-54 ◽  
Author(s):  
Katsunori Mizuno ◽  
Kazuki Abukawa ◽  
Toru Kashima ◽  
Akira Asada ◽  
Yasufumi Fujimoto ◽  
...  
Keyword(s):  
High Resolution ◽  
Acoustic Imaging ◽  
Imaging Sonar ◽  
Whooper Swan

Benthic fish behavior characterization with a mechanically scanned imaging sonar

2019 ◽  
Vol 84 ◽  
pp. 1-11 ◽  
Author(s):  
Shih-Liang Tsao ◽  
Wen-Miin Tian ◽  
Chih-Yung Shen ◽  
Tai-Yueh Chen
Keyword(s):  
Fish Behavior ◽  
Benthic Fish ◽  
Imaging Sonar
Sign in / Sign up

Export Citation Format

Share Document