CN113989257A - Electric power comprehensive pipe gallery settlement crack identification method based on artificial intelligence technology - Google Patents
Electric power comprehensive pipe gallery settlement crack identification method based on artificial intelligence technology Download PDFInfo
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Abstract
The invention discloses an artificial intelligence technology-based electric power comprehensive pipe gallery settlement crack identification method, which comprises the following steps: (1) acquiring a wall image of a pipe gallery; (2) preprocessing the wall image of the pipe gallery, analyzing the uneven illumination condition by utilizing the gray value of the image, and performing histogram equalization correction processing; (3) extracting image edge characteristics of the preprocessed image; (4) carrying out BP neural network training by adopting a back propagation algorithm; (5) and inputting the extracted image edge characteristics into the trained BP neural network and outputting a detection and identification result. The invention provides an improved back propagation algorithm suitable for detecting pipe gallery wall cracks in a complex environment, a large amount of image data collected in a power comprehensive pipe gallery, including crack pictures and normal pictures of walls in an internal structure of the pipe gallery, are learned and trained, and finally detection and identification of pipe gallery settlement cracks are achieved.
Description
Technical Field
The invention relates to the technical field of electric power comprehensive pipe rack crack identification, in particular to an electric power comprehensive pipe rack settlement crack identification method based on an artificial intelligence technology.
Background
Underground electric power utility tunnel or underground electric power utility pipeline are in long-term service in-process, because surrounding ground environment is complicated, receive different geological environment, ground building, the river, highway, the railway, piping lane structural material performance, and the long-term effect of ground load, the combined action of factors such as fatigue effect and sudden change effect, structural damage accumulation will inevitably appear in piping lane or pipe wall, can cause the piping lane to subside or the condition such as crack to a certain extent, lead to piping lane structural failure and piping lane to collapse even under extreme condition, cause major safety accident. Consequently, subside and take place the pipe wall crack that appears after the piping lane subsides and carry out intelligent monitoring to utility power pipe gallery, have the great significance to utility power pipe gallery safety operation and reduce the emergence that the piping lane subsides the accident.
According to experience, when the power utility tunnel takes place to subside, the crack can appear in the inside wall structure of general piping lane. With the rapid development of the industry 4.0 and the smart manufacturing industry, the artificial intelligence technology is increasingly applied to industrial safety production, robot smart inspection and other important fields. Wherein, intelligence inspection robot is to underground electric power utility tunnel safety monitoring, is an important application of artificial intelligence technique in the electric power industry, but current crack identification method is difficult to more accurate observe the crack, and monitoring effect can not obtain better assurance, uses to have the drawback, and can't be adapted to under the complex environment piping lane wall crack detection.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide the electric power comprehensive pipe gallery settlement crack identification method based on the artificial intelligence technology, a back propagation algorithm is adopted for BP neural network training, the trained BP neural network is used for detecting and identifying the image edge characteristics of the acquired pipe gallery wall image, the crack of the pipe gallery wall is detected, and the real-time monitoring of the settlement of the internal structure of the electric power comprehensive pipe gallery is realized.
The invention adopts the following technical scheme.
An electric power comprehensive pipe gallery settlement crack identification method based on an artificial intelligence technology comprises the following steps:
(1) acquiring a wall image of a pipe gallery;
(2) preprocessing the wall image of the pipe gallery, analyzing the uneven illumination condition by utilizing the gray value of the image, and performing image histogram equalization correction processing;
(3) extracting image edge characteristics of the preprocessed image;
(4) carrying out BP neural network training by adopting a back propagation algorithm;
(5) and inputting the extracted image edge characteristics into the trained BP neural network and outputting a detection and identification result.
Further, in the step (2), the image gray-scale value analysis method includes: and converting the acquired color image into a binary gray image, respectively projecting pixels of the gray image row by row and column by column, calculating the sum of all pixels in each row and each column in the gray image, and obtaining the uneven distribution of illumination through the step change of gray projection values in the horizontal direction and the vertical direction.
Further, in the step (2), an image histogram equalization method is adopted to perform correction processing of illumination uneven distribution on the gray level image;
the mathematical expression for the image histogram equalization is:
where n is the sum of the image pixels, niIs the sum of the pixels of the ith gray level, riIs the ith gray level, i-0, 1,2, …, L-1.
Further, in the step (3), the extraction of the image edge features is performed by extracting Canny edge features of the image from the pipe gallery wall image.
Further, the Canny edge feature extraction step is as follows:
(3.1) performing Gaussian smoothing on the input image by using a Gaussian kernel function;
(3.2) calculating the gradient amplitude and direction of the image by using a Sobel operator;
(3.3) according to the gradient direction, carrying out non-maximum suppression on the gradient amplitude;
and (3.4) carrying out double-threshold processing on the image after the non-extreme value large suppression, linking edges into the contour in the high-threshold image, searching points meeting a low threshold in the neighborhood points of the broken points when the end point of the contour is reached, and collecting new edges according to the points until the edge of the whole image is closed.
Further, in the step (4), historically collected crack pictures and normal pictures containing the internal structure of the pipe gallery are subjected to image preprocessing and edge feature extraction to form a training sample; and training the BP neural network by adopting the sample data through a back propagation algorithm.
Further, the back propagation algorithm starts from the output unit and propagates the weight correction caused by the total error back to the hidden layer unit; using output layersPartial derivative of each neuron deltao(k) And the output h of each neuron of the hidden layerho(k) Calculating partial derivative delta w of error function to each neuron of hidden layerho(k) Using the output delta of each neuron of the hidden layerh(k) And input parameters of neurons of the input layer
And correcting the connection weight value.
Further, learning the weight of the multilayer BP neural network by using a back propagation algorithm, weighting a plurality of output units comprehensively, and recalculating the reverse error E, so as to add the error weights output by all the networks, wherein the formula is as follows:
where D is the total number of training samples, outputs are the set of output units, wiIs a weighting coefficient, tkdAnd OkdThe output values associated with the training sample d and the kth output unit are determined by the corresponding input values, the hidden function, and the excitation function.
Further, a gradient descent algorithm is adopted in the back propagation algorithm, and the square error between the output value of the BP neural network output unit and the target value is minimized.
Further, in the step (5), the extracted edge image is converted into an initial value of neuron input, where each neuron unit has a certain number of real-valued inputs and generates a single real-valued output, and a final output result of the neuron is shown as follows:
wherein f is the excitation function in the neural network, and the input units in the neurons are x respectivelyiCorresponding to a weighting coefficient of wi，i＝1,2,3,…,n-1。
Compared with the prior art, the method has the beneficial effects that the improved back propagation algorithm suitable for detecting the pipe gallery wall cracks in the complex environment is provided, a large amount of image data collected in the power comprehensive pipe gallery, including the crack pictures and normal pictures of the wall in the internal structure of the pipe gallery, are learned and trained, and finally, the detection and identification of the pipe gallery settlement cracks are realized.
Considering the relevance among all units, the back propagation algorithm adopts a gradient descent method, reduces the square error between the network output value and the target value, comprehensively weights a plurality of output units, recalculates the reverse error E, and thus weights and adds the errors of all network outputs.
The uneven illumination of the pipe gallery wall image is analyzed by utilizing the image gray value, the uneven illumination is weakened through image light correction, and an excessively dark or excessively bright area can be corrected to the original natural color.
Drawings
FIG. 1 is a flow chart of an electric power comprehensive pipe gallery settlement crack identification method based on an artificial intelligence technology;
FIG. 2 is a schematic diagram of a three-layer BP neural network;
FIG. 3 is a schematic diagram of a neuron structure in a BP neural network;
FIG. 4 is a picture of a tube lane crack;
FIG. 5 is a schematic diagram of tube lane crack edge feature extraction;
figure 6 is electric utility tunnel settlement crack detection effect schematic diagram.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the method for identifying settlement cracks of the power utility tunnel based on the artificial intelligence technology comprises the following steps:
(1) acquiring a wall image of the pipe gallery as image input;
patrol and examine the robot through the intelligence and carry out real-time shooting to pipe gallery wall under the complicated hazardous environment, acquire pipe gallery wall image, as shown in figure 4.
(2) Preprocessing the wall image of the pipe gallery;
and analyzing the acquired pipe gallery wall image by using the image gray value and carrying out correction processing. Analyzing the illumination distribution condition of the image on the wall of the pipe gallery by utilizing the gray value of the image, acquiring areas with uneven illumination, including areas with over-dark or over-bright areas, correcting the uneven condition of the image light by a gradient histogram method, and correcting the areas with over-dark or over-bright areas to the original natural color.
Pipe rack wall surrounding environment is complicated, and various power equipment install inside the pipe rack, cause the robot to shelter from because the light of replenishing the light source is sheltered from by the power equipment of installation in the pipe rack when patrolling and examining, lead to appearing light on the wall and shelter from the edge, influence the extraction at the true edge of wall.
And judging whether the region of interest has gray shade or not by analyzing the gray projection value of the edge of the region of interest by using an image gray value analysis method.
Firstly, converting the obtained color image into a binary gray image, then respectively projecting pixels of the image row by row and column by column, and calculating the sum of all pixels in each row and each column in the image. For a region of interest of an m × n size image I (x, y), a pixel grayscale projection value in the I-th row horizontal direction is calculated:
pixel gray projection value in the j-th column in the vertical direction:
by analyzing the gray projection values in the horizontal direction and the vertical direction, if the pixel gray projection values in any direction have obvious step changes, the step changes of the image gray values caused by the shielding of the projection light can be considered to exist.
Based on the analysis, an image histogram method is utilized to perform histogram equalization on the image, and the interference edge caused by the fact that the light is shielded is eliminated as much as possible.
The image histogram is a statistical relationship representing the frequency of occurrence of each gray level in the digital image. The histogram can give a general description of the gray scale range of the image, the frequency of each gray scale and the distribution of the gray scale, the average brightness and contrast of the whole image, and the like. Assuming that the gray scale range of the gray image is [0, L-1], its histogram is defined as:
p(rk)＝nk/n
where n is the sum of the image pixels, nkIs the sum of pixels of the kth gray level, rkIs the kth gray level, k being 0,1,2, …, L-1.
The mathematical expression for histogram equalization of an image is as follows:
the histogram equalization is carried out on the image by using the above formula, namely, the equalization of the gray value of the image can be realized, and the influence of the interference edge caused by light shielding on the extraction of the real crack edge of the pipe gallery wall is reduced.
(3) Extracting image edge characteristics of the preprocessed image;
as shown in fig. 5, the image edge feature extraction is performed on the preprocessed pipe gallery wall image, and the extracted edge feature is used as the input of the BP neural network by analyzing the edge feature of the pipe gallery wall structure.
The extraction of image edge features is carried out on the pipe gallery wall image by extracting Canny edge features of the image, and the extracted edge features are used as input of a BP neural network input unit.
The Canny edge extraction algorithm is as follows:
firstly, Gaussian smoothing is carried out on an input image, smoothing of noise points is achieved by utilizing a Gaussian kernel function, interference of various noises on image feature extraction is reduced, and edge error rate generated by image noises is reduced. The gaussian kernel function is defined as follows:
where σ is the standard deviation, the size is set to 0.6 for the canal corridor image, and the gaussian smoothing window size is set to 7 × 7 pixels.
Then, the gradient magnitude and direction are calculated to estimate the edge strength and direction at each point of the image. Convolving the input image by using a Sobel operator, assuming that the original image is I (x, y), and Sobel gradient descriptors in the horizontal direction and the vertical direction are respectively:
then, with the convolution operation on the image I (x, y):
where is the sign of the convolution.
The Sobel gradient magnitude of image I (x, y) is then:
G＝|Gx|+|Gy|
secondly, according to the gradient direction, the non-maximum value suppression is carried out on the gradient amplitude. For each pixel point, the gradient direction of the pixel point is approximate to one of the following values (0,45,90,135,180,225,270,315), the gradient strength of the pixel point and the gradient direction of the pixel point in the positive and negative directions is compared, if the gradient strength of the pixel point is maximum, the pixel point is kept, and if the gradient strength of the pixel point is maximum, the pixel point is inhibited (deleted, namely set to be 0).
Finally, edges are processed and connected with dual thresholds. After the non-extreme value large suppression, a plurality of noise points still exist in the image. And (3) processing by using double thresholds, namely setting an upper threshold and a lower threshold, considering that pixel points in the image are bound if the pixel points are larger than the upper threshold, considering that the pixel points are not bound if the pixel points are smaller than the lower threshold, and considering that the pixel points are candidates between the pixel points and the lower threshold. By a weak boundary connected to a strong boundary being considered a boundary, other weak boundaries are suppressed. The edges are linked into the contour in the high-threshold image, when the end point of the contour is reached, the algorithm searches for a point meeting the low threshold in the neighborhood of the broken point, and then collects new edges according to the point until the edge of the whole image is closed.
(4) Carrying out BP neural network training by adopting a back propagation algorithm;
a large amount of historically acquired image data in the power comprehensive pipe rack, including crack pictures and normal pictures in the internal structure of the pipe rack, are subjected to image preprocessing and edge feature extraction to form a training sample D; and training the BP neural network by adopting the sample data through a back propagation algorithm.
The BP neural network is composed of a plurality of neuron units to form a huge multi-layer BP neural network system. The neuron unit comprises an input unit, an output unit and one or more hidden layer units. Taking three-layer BP neural network as an example, the structure is shown in FIG. 2, xiIs an input value, yiThe output value is i ═ 1,2, and 3.
The back propagation algorithm starts from the output unit and propagates the weight correction caused by the total error back to the hidden layer unit.
First, the partial derivative delta of each neuron in the output layer is usedo(k) And the output h of each neuron of the hidden layerho(k) Calculating partial derivative delta w of error function to each neuron of hidden layerho(k)：
Δwho(k)＝-μδo(k)hho(k)
Wherein mu is a corresponding proportionality coefficient.
Using the output delta of each neuron of the hidden layerh(k) And input parameters of neurons of the input layer
Correcting the connection weight:
wherein eta is a corresponding proportionality coefficient.
Considering the relevance among the units of the BP neural network, learning the weight of the multilayer BP neural network by using a back propagation algorithm, comprehensively weighting a plurality of output units, and recalculating the reverse error E, thereby weighting and adding the errors output by all the networks, wherein the formula is as follows:
where D is the total number of training samples, outputs are the set of output units, wiIs a weighting coefficient, tkdAnd OkdThe output values associated with the training sample d and the kth output unit are determined by the corresponding input values, the hidden function, and the excitation function.
The hidden function and the excitation function used in the present invention are defined as follows, respectively:
suppose we want to hide the parameter W of a layer to the k-th layer(k)And partial derivative b(k). Suppose z(k)The input representing the k-th layer neurons, i.e., defining the hidden function at this time, is:
z(k)＝W(k)*n(k-1)+b(k)
sigmoid excitation function:
and a gradient descent method is adopted in the back propagation algorithm, so that the square error between the output value of the BP neural network output unit and the target value is reduced.
In the gradient descent algorithm, the error function is set as:
J＝0.5(y-o)2＝0.5(y-f(∑Wixi))2
where y is the actual result, o is the predicted result, and o ═ f (∑ W)ixi) Results of weighted outputs for various input functions in a neural network, WiWeighting coefficients, x, for corresponding input channelsiThe neuron inputs a value.
The gradient descent algorithm process is to minimize the function J and solve the corresponding WiThe process of (1). Defining the rate of change as α, then:
by using the above formula, W can be obtained through a plurality of operationsiThe purpose of reducing errors is achieved by using the gradient descent algorithm.
(5) Inputting the extracted image edge characteristics into a trained BP neural network and outputting a detection recognition result;
and detecting and identifying the obtained image edge characteristics by using the trained BP neural network, judging whether the wall has cracks, and finally outputting a detection result and early warning information, as shown in FIG. 6.
As shown in fig. 3, the BP neural network converts the extracted edge image into an initial neuron input value according to the edge feature extracted from the image, where each neuron unit has a certain number of real inputs and generates a single real value output, and the final output result of the neuron is shown as follows:
wherein f is the excitation function in the neural network, and the input units in the neurons are x respectivelyiCorresponding to a weighting coefficient of wi，i＝1,2,3,…,n-1。
Compared with the prior art, the method has the beneficial effects that the improved back propagation algorithm suitable for detecting the pipe gallery wall cracks in the complex environment is provided, a large amount of image data collected in the power comprehensive pipe gallery, including the crack pictures and normal pictures of the wall in the internal structure of the pipe gallery, are learned and trained, and finally, the detection and identification of the pipe gallery settlement cracks are realized.
Considering the relevance among all units, the back propagation algorithm adopts a gradient descent method, reduces the square error between the network output value and the target value, comprehensively weights a plurality of output units, recalculates the reverse error E, and thus weights and adds the errors of all network outputs.
The uneven illumination of the pipe gallery wall image is analyzed by utilizing the image gray value, the uneven illumination is weakened through image light correction, and an excessively dark or excessively bright area can be corrected to the original natural color.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (10)
1. The method for identifying the settlement cracks of the power comprehensive pipe gallery based on the artificial intelligence technology is characterized by comprising the following steps of:
(1) acquiring a wall image of a pipe gallery;
(2) preprocessing the wall image of the pipe gallery, analyzing the uneven illumination condition by utilizing the gray value of the image, and performing image histogram equalization correction processing;
(3) extracting image edge characteristics of the preprocessed image;
(4) carrying out BP neural network training by adopting a back propagation algorithm;
(5) and inputting the extracted image edge characteristics into the trained BP neural network and outputting a detection and identification result.
2. The artificial intelligence technology-based electric power utility tunnel settlement fracture identification method according to claim 1,
in the step (2), the image gray-scale value analysis method includes: and converting the acquired color image into a binary gray image, respectively projecting pixels of the gray image row by row and column by column, calculating the sum of all pixels in each row and each column in the gray image, and obtaining the uneven distribution of illumination through the step change of gray projection values in the horizontal direction and the vertical direction.
3. The artificial intelligence technology-based electric power utility tunnel settlement fracture identification method according to claim 2,
in the step (2), an image histogram equalization method is adopted to correct uneven illumination distribution of the gray level image;
the mathematical expression for the image histogram equalization is:
where n is the sum of the image pixels, niIs the sum of the pixels of the ith gray level, riIs the ith gray level, i-0, 1,2, …, L-1.
4. The artificial intelligence technology-based electric power utility tunnel settlement fracture identification method according to claim 1,
in the step (3), the extraction of the image edge features is carried out on the pipe gallery wall image by extracting the Canny edge features of the image.
5. The artificial intelligence technology-based electric power utility tunnel settlement fracture identification method according to claim 4,
the Canny edge feature extraction steps are as follows:
(3.1) performing Gaussian smoothing on the input image by using a Gaussian kernel function;
(3.2) calculating the gradient amplitude and direction of the image by using a Sobel operator;
(3.3) according to the gradient direction, carrying out non-maximum suppression on the gradient amplitude;
and (3.4) carrying out double-threshold processing on the image after the non-extreme value large suppression, linking edges into the contour in the high-threshold image, searching points meeting a low threshold in the neighborhood points of the broken points when the end point of the contour is reached, and collecting new edges according to the points until the edge of the whole image is closed.
6. The artificial intelligence technology-based electric power utility tunnel settlement fracture identification method according to claim 1,
in the step (4), historically collected crack pictures and normal pictures in the internal structure of the pipe gallery are subjected to image preprocessing and edge feature extraction to form a training sample; and training the BP neural network by adopting the sample data through a back propagation algorithm.
7. The artificial intelligence technology-based electric power utility tunnel settlement fracture identification method according to claim 6,
the back propagation algorithm starts from the output unit and propagates the weight correction caused by the total error back to the hidden layer unit; using partial derivatives delta of neurons in the output layero(k) And the output h of each neuron of the hidden layerho(k) Calculating partial derivative delta w of error function to each neuron of hidden layerho(k) Using the output delta of each neuron of the hidden layerh(k) And input parameters of neurons of the input layer
And correcting the connection weight value.
8. The artificial intelligence technology-based electric power utility tunnel settlement fracture identification method according to claim 7,
learning the weight of the multilayer BP neural network by using a back propagation algorithm, weighting a plurality of output units comprehensively, and recalculating the reverse error E, thereby weighting and adding the errors output by all the networks, wherein the formula is as follows:
where D is the total number of training samples, outputs are the set of output units, wiIs a weighting coefficient, tkdAnd OkdThe output values associated with the training sample d and the kth output unit are determined by the corresponding input values, the hidden function, and the excitation function.
9. The artificial intelligence technology-based electric power utility tunnel settlement fracture identification method according to claim 7,
a gradient descent algorithm is adopted in the back propagation algorithm, and the square error between the output value of the BP neural network output unit and the target value is minimized.
10. The artificial intelligence technology-based electric power utility tunnel settlement fracture identification method according to claim 1,
in the step (5), the extracted edge image is converted into an initial neuron input value, wherein each neuron unit has a certain number of real value inputs and generates a single real value output, and a final output result of the neuron is shown as the following formula:
wherein f is the excitation function in the neural network, and the input units in the neurons are x respectivelyiCorresponding to a weighting coefficient of wi，i＝1,2,3,…,n-1。
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| PCT/CN2021/138598 WO2023082418A1 (en) | 2021-11-09 | 2021-12-16 | Power utility tunnel settlement crack identification method based on artificial intelligence technology |
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| CN116630813B (en) * | 2023-07-24 | 2023-09-26 | 青岛奥维特智能科技有限公司 | Highway road surface construction quality intelligent detection system |
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