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Belt longitudinal tearing detection method based on neural network

A neural network and convolutional neural network technology, applied in the field of production monitoring, can solve the problems of easy misidentification and low accuracy, and achieve the effect of convenient viewing and high accuracy

Pending Publication Date: 2022-03-01
BEIJING HUANENG XINRUI CONTROL TECH
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Problems solved by technology

However, the inventors found that the calculation method based on the pixel gray value has low accuracy and is prone to misidentification

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  • Belt longitudinal tearing detection method based on neural network
  • Belt longitudinal tearing detection method based on neural network

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Embodiment Construction

[0020] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0021] Various aspects and features of the invention are described herein with reference to the accompanying drawings.

[0022] These and other characteristics of the invention will become apparent from the following description of preferred forms of embodiment given as non-limiting examples with reference to the accompanying drawings.

[0023] It should also be understood that while the invention has been described with reference to a few specific examples, those skilled in the art can certainly implement many other equivalent forms of the invention, which have the features described in the claims and thus lie within the scope of the present invention. within the limited scope of protection.

[0024] The above and other aspects...

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Abstract

The invention relates to a belt longitudinal tear detection method based on a neural network, and the method comprises the steps: S1, constructing a convolutional neural network algorithm model which comprises a first convolutional network and a second convolutional network; s2, training the convolutional neural network algorithm model through a preset training set, wherein the preset training set comprises a first picture set containing longitudinal tear features and a second picture set containing frame labeling on the longitudinal tear features; s3, collecting image information of a coal conveying belt area; s4, inputting the image information into the convolutional neural network algorithm model; and S5, judging whether longitudinal tearing characteristics exist in the image information or not, and outputting a picture frame containing a frame label. Compared with a common algorithm based on edge detection or pixel recognition, the belt longitudinal tear detection method based on the neural network has the advantage of being high in accuracy.

Description

technical field [0001] The invention relates to a production monitoring method, in particular to a neural network-based belt longitudinal tear detection method. Background technique [0002] Coal conveying belt conveyor is an important equipment for fuel transportation in thermal power plants. Coal conveying belts are generally divided into ordinary belts and steel cord belts. Thermal power plants usually use ordinary belts with a bandwidth of about 1-1.4m. As the bandwidth increases, the price will become more and more expensive. Due to the fast running speed of the belt conveyor, sometimes the belt is torn due to equipment or other reasons during transportation. Once it occurs, the tear range is generally tens of meters or hundreds of meters in length, which may cause Economic losses range from tens of thousands to hundreds of thousands, and at the same time may directly affect the normal operation of the unit. Although some units have installed anti-belt tearing devices...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/10G06V10/764G06V10/774G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/2411G06F18/214
Inventor 田宏哲赵霞孙新佳刘畅苏睿之谭泽莹杨洋
Owner BEIJING HUANENG XINRUI CONTROL TECH
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