Flow velocity monitoring implementation method based on adversarial generative network

An implementation method and network technology, applied in the field of pattern recognition, can solve problems such as sampling image noise and aggravating the difficulty of classification, and achieve the effects of high accuracy, improved classification accuracy, and improved robustness

Inactive Publication Date: 2017-07-14
ZHEJIANG UNIV OF TECH
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Problems solved by technology

In addition, on-site nodes are often disturbed by unfavorable factors such as rain, fog, and light, resulting in a large amount of noise in the sampled image, which greatly increases the difficulty of classification

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  • Flow velocity monitoring implementation method based on adversarial generative network
  • Flow velocity monitoring implementation method based on adversarial generative network
  • Flow velocity monitoring implementation method based on adversarial generative network

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[0061] Image preprocessing: Since the monitoring points are outdoors, the shooting of water flow images is inevitably affected by factors such as weather (such as rain, snow, and fog) and light changes. In order to weaken the influence of these factors on the image quality, the RGB image of the water flow image is converted into a grayscale image and histogram equalization is performed to enhance the contrast so that the outline of the water pattern becomes obvious. Figure 2(a) and 2(b) They are the grayscale image and the image processed by histogram equalization. According to the historical data of the monitoring point, 10 flow velocity intervals are predefined (the number of intervals can be increased or decreased according to the accuracy requirements in practical applications), which are 0-0.25m / s, 0.25-0.5m / s, 0.5-0.75 m / s, 0.75-1.0m / s, 1.0-1.25m / s, 1.25-1.5m / s, 1.5-2.0m / s, 2.0-2.5m / s, 2.5-3.0m / s, 3m / s and Above, each flow velocity interval contains 30 water flow images...

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Abstract

The invention provides a flow velocity monitoring implementation method based on an adversarial generative network. The flow velocity monitoring implementation method comprises the following steps that (1) water flow image preprocessing is performed; (2) image classification is performed based on the adversarial generative network; (3) flow velocity determination: the image classification results and flow velocity intervals are corresponding in a one-to-one way; and (4) state analysis: a state abnormal signal is transmitted when the monitoring result indicates that the flow velocity exceeds the preset threshold. The beneficial effects mainly reside in that the advantages of discriminant and generative classification algorithms are effectively combined in adversarial training of a generator and a discriminator and unsupervised learning is realized, and the synthetic water flow image outputted by the generator of the adversarial generative network and the real image act as the input of the discriminator together so that the robustness of a classifier for the noised water flow image can be greatly enhanced, classification is performed according to the water flow image and rapid flow velocity determination can be realized in a way of being corresponding to the preset flow velocity intervals and classified management of mass water flow information is facilitated.

Description

technical field [0001] The invention relates to a method for realizing flow velocity monitoring, in particular to a method for realizing flow velocity monitoring based on confrontation generation networks, and belongs to the field of pattern recognition. Background technique [0002] Water flow velocity monitoring can be used directly or indirectly for intelligent dispatching of hydropower stations, hydrological monitoring and disaster forecasting. It is necessary for water conservancy project planning and design, flood control and drought relief, and irrigation production. Fast and accurate flow velocity monitoring can significantly improve the scientific nature of water conservancy project dispatching. and the predictability of drought and flood disasters. Flow velocity monitoring based on image recognition has the advantages of low cost and high precision, and its core technology lies in image recognition. Classification algorithms for image recognition can be roughly di...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G01P5/00
CPCG01P5/00G06F18/2415
Inventor 王万良李卓蓉杨胜兰邱虹张兆娟
Owner ZHEJIANG UNIV OF TECH
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