Water quality monitoring method based on feedback self correcting dense connected convolution network

Abstract

This paper presents a method of water quality monitoring using a Feedback self correcting system combined with a Densely Connected Convolution Network. We find an effective method to correct the model output and innovate the method of biological water quality monitoring. Fish movement trajectory is a comprehensive expression of various water quality classification characteristics used in all the literature, and it is an important basis for classification of biological water quality. In this paper, we use 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. The trajectory image data sets are divided into normal and abnormal water quality. Densely connected convolution network(DenseNet) is used to classify the quality of water. The experiment is based on normal and abnormal water quality image data, and the model correction system with deviation feedback can be designed by the output of softmax. The learning ability of the classification model in practical application is greatly improved and enhance the stability of the detection system. The experimental results show that the water quality identification rate of the model reaches 99.38%, which is far higher than that of all previous water quality classification models.