Research Article | OPEN ACCESS
Implementation of Water Quality Management by Fish School Detection Based on Computer Vision Technology
Yan Hou
College of Electrical Engineering, Henan University of Technology, Henan 450001, China
Advance Journal of Food Science and Technology 2015 6:422-427
Received: March ‎14, ‎2015 | Accepted: March ‎24, ‎2015 | Published: August 25, 2015
Abstract
To solve the detection of abnormal water quality, this study proposed a biological water abnormity detection method based on computer vision technology combined with Support Vector Machine (SVM). First, computer vision is used to acquire the parameters of fish school motion feature which can reflect the water quality and then these parameters were preprocessed. Next, the sample set is established and the water quality abnormity monitoring model based on computer vision technology combined with SVM is acquired. At last, the model is used to analyze and evaluate the motion characteristic parameters of fish school under unknown water, in order to indirectly monitor the situation of water quality. In view of great influence of kernel function and parameter optimization to the model, this study compared different kinds of kernel function and then made optimization selection using Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and grid search. The results obtained demonstrate that, that method is effective for monitoring water quality abnormity.
Keywords:
Computer vision technology, fish school motion, support vector machine,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2042-4876
ISSN (Print): 2042-4868 |
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