基于卷积神经网络的生物式水质监测方法

doi:10.3969/j.issn.1000-1158.2019.04.29

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Abstract Biological water quality monitoring is usually through the extraction of water stress response characteristics in different environments, and then feature classification, so as to identify water quality. Aiming at the problem of water quality monitoring, method of CNN convolution neural network was presented. Fish trajectory is a comprehensive expression of the various water quality classification characteristics used in all the literatures and is an important basis for the classification of biological water quality. Using the image segmentation method of Mask-RCNN to obtain the centroid coordinates of the fish and draw the trajectory image of the fish in a certain period of time. Two sets of trajectory image data sets under normal and abnormal water quality were produced. The Inception-v3 network serves as a feature preprocessing part of the data set, the CNN convolution neural network was reestablished to classify the features extracted by Inception-v3 network. Set up multiple sets of parallel experiments to classify normal and abnormal water quality in different environments. The results showed that the CNN convolution neural network model had a water quality recognition rate of 99.38%, which met the requirements of water quality identification.

Key words： metrology biological water quality monitoring convolution neural network Mask-RCNN method

Received: 10 April 2018 Published: 10 June 2019

PACS:

TB99

Corresponding Authors: CHENG Shuhong E-mail: shhcheng@ysu.edu.cn

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	CHENG Shu-hong
	ZHANG Shi-jun
	ZHAO Kao-peng

Cite this article:

CHENG Shu-hong,ZHANG Shi-jun,ZHAO Kao-peng. Biological Water Quality Monitoring Method Based on Convolution Neural Network[J]. Acta Metrologica Sinica, 2019, 40(4): 721-727.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.04.29 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I4/721

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