scholarly journals Robust Underwater Fish Detection Using an Enhanced Convolutional Neural Network

2021 ◽  
Vol 13 (3) ◽  
pp. 44-54
Author(s):  
Dipta Gomes ◽  
◽  
A.F.M. Saifuddin Saif
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fish Detection

scholarly journals A Comparison of vision-based and CNN-based detector for fish monitoring in complex environment

2021 ◽  
Vol 15 (2) ◽  
pp. 232-244
Author(s):  
Yijun Ling ◽  
Phooi Yee Lau
Keyword(s):  
Neural Network ◽  
Environmental Impact ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Complex Environment ◽  
System Framework ◽  
Underwater Cameras ◽  
Fish Detection ◽  
Fish Monitoring ◽  
Extract Information

Aquaculture farming can help soften the environmental impact of overfishing by fulfilling seafood demands with farmed fishes. However, to maintain big scale farms can be challenging, even with the help of underwater cameras affixed in farm cages, because there are hours’ worth of footages to sift through, which can be a laborious task if performed manually. Vision-based system therefore could be deployed to filter useful information from video footages, automatically. This work proposed to solve the above mentioned problems by deploying the; 1) Extended UTAR Aquaculture Farm Fish Monitoring System Framework (UFFMS), being the handcrafted method, and 2) Faster Region Convolutional Neural Network (Faster RCNN), being the CNN-based method, for fish detection. These two methods could extract information about fishes from video footages. Experimental results show that for well-lit footages, Faster RCNN performs better, compared to the extended-UFFMS. However, accuracy of Faster RCNN drops drastically for non-well-lit footages, at an average of 28.57%, despite still having perfect precision scores. 

scholarly journals Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 222
Author(s):  
Teh Hong Khai ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Mohammad Kamrul Hasan ◽  
Ahmad Tarmizi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Image Data ◽  
Fish Farming ◽  
Accuracy Rate ◽  
Counting Procedure ◽  
Hatching Process ◽  
Detection And Counting ◽  
Fish Detection

Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly depended on the use of non-machine learning-based and machine learning-based counting methods and so was unable to provide precise results. In this work, we used a robotic eye camera to capture shrimp photos on a shrimp farm to train the model. The image data were classified into three categories based on the density of shrimps: low density, medium density, and high density. We used the parameter calibration strategy to discover the appropriate parameters and provided an improved Mask Regional Convolutional Neural Network (Mask R-CNN) model. As a result, the enhanced Mask R-CNN model can reach an accuracy rate of up to 97.48%.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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