期刊名称：Advance Journal of Food Science and Technology
印刷版ISSN：2042-4868
电子版ISSN：2042-4876
出版年度：2015
卷号：9
期号：6
页码：422-427
DOI：10.19026/ajfst.9.1896
出版社：MAXWELL Science Publication
摘要：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.
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