CN114596262A - Dam monitoring and analyzing method and system based on image recognition technology - Google Patents
Dam monitoring and analyzing method and system based on image recognition technology Download PDFInfo
- Publication number
- CN114596262A CN114596262A CN202210099656.8A CN202210099656A CN114596262A CN 114596262 A CN114596262 A CN 114596262A CN 202210099656 A CN202210099656 A CN 202210099656A CN 114596262 A CN114596262 A CN 114596262A
- Authority
- CN
- China
- Prior art keywords
- dam
- panoramic image
- detection model
- recognition technology
- crack detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000012544 monitoring process Methods 0.000 title claims abstract description 36
- 238000005516 engineering process Methods 0.000 title claims abstract description 27
- 238000001514 detection method Methods 0.000 claims abstract description 37
- 238000007781 pre-processing Methods 0.000 claims abstract description 22
- 238000000605 extraction Methods 0.000 claims abstract description 18
- 230000004927 fusion Effects 0.000 claims abstract description 15
- 230000000739 chaotic effect Effects 0.000 claims abstract description 11
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 8
- 230000002068 genetic effect Effects 0.000 claims abstract description 8
- 230000011218 segmentation Effects 0.000 claims description 23
- 230000009467 reduction Effects 0.000 claims description 21
- 238000009499 grossing Methods 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 11
- 238000013528 artificial neural network Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 claims description 4
- 230000015654 memory Effects 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 238000007667 floating Methods 0.000 claims description 3
- 238000011176 pooling Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims 3
- 238000005457 optimization Methods 0.000 claims 1
- 230000008569 process Effects 0.000 abstract description 9
- 230000007547 defect Effects 0.000 abstract description 7
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 238000013135 deep learning Methods 0.000 abstract description 2
- 238000004590 computer program Methods 0.000 description 5
- 230000035772 mutation Effects 0.000 description 5
- 238000006073 displacement reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000007787 long-term memory Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000006403 short-term memory Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/047—Probabilistic or stochastic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30132—Masonry; Concrete
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Linguistics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Software Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Physiology (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a dam monitoring and analyzing method and a dam monitoring and analyzing system based on an image recognition technology, wherein the dam monitoring and analyzing method based on the image recognition technology comprises the following steps: preprocessing the acquired dam panoramic image through a preprocessing module; performing feature extraction on the preprocessed image by using a feature extraction module, and sending the features to a feature fusion module; the characteristic fusion module establishes a dam crack detection model according to the characteristics, and then optimizes the dam crack detection model based on a chaotic genetic algorithm; inputting the dam panoramic image into the optimized dam crack detection model, and identifying the cracks of the dam in real time; the method is based on artificial intelligence technologies such as machine vision, deep learning and the like, and can accurately detect the defects of the dam in the operation process by utilizing the computer to process, analyze and understand the images.
Description
Technical Field
The invention relates to the technical field of dam detection, in particular to a dam monitoring analysis method and a dam monitoring analysis system based on an image recognition technology.
Background
As the water conservancy project plays an increasingly important role in the Chinese energy structure, the dam safety problem is increasingly prominent, so the dam safety monitoring field comes across. The specific mode is that a specific monitoring instrument is arranged in the dam body or on the outer surface of the dam body during and after the dam body is constructed, and the specific monitoring instrument is respectively responsible for different monitoring objects, such as horizontal displacement along the river direction, horizontal displacement along the river direction on the surface of the dam body or settlement of the measuring point position of the dam body in the vertical direction and the like.
The analysis of the monitoring data of the first-level dam of the Toyoxi mainly comprises the steps that operators carry out manual processing and calculation analysis by means of simple analysis software and data processing tools, and a system cannot automatically analyze data and generate an analysis report. And images acquired by the traditional high-definition camera need to be manually identified and analyzed, so that the requirement on the professional performance of workers is high, and the workload is huge. The automatic image processing and analysis mainly comprises the steps of automatically carrying out contrast analysis and information extraction on a shot image by utilizing an image recognition technology, accurately finding defects in time, qualitatively and quantitatively calculating and analyzing the defects, reducing the workload and the working strength of workers to the maximum extent, and greatly improving the working efficiency and the working quality. Therefore, a method of more excellent performance is urgently required to cope with these problems.
The traditional monitoring analysis is usually separated from a building body, data analysis is carried out only, the connection with engineering is not tight enough, and data of a single monitoring point are modeled and calculated. The single-measuring-point model cannot meet the requirement of overall monitoring of the dam because the cost is high, the safety of the structure needs to be ensured, and the position arrangement of measuring points arranged inside and on the surface of the dam body is very sparse.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and title of the application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
In order to solve the above technical problems, the present invention provides the following technical solutions, including: preprocessing the acquired dam panoramic image through a preprocessing module; performing feature extraction on the preprocessed image by using a feature extraction module, and sending the features to a feature fusion module; the characteristic fusion module establishes a dam crack detection model according to the characteristics, and then optimizes the dam crack detection model based on a chaotic genetic algorithm; and inputting the dam panoramic image into the optimized dam crack detection model, and identifying the cracks of the dam in real time.
As a preferable scheme of the dam monitoring and analyzing method based on the image recognition technology, the method comprises the following steps: the preprocessing module comprises a smoothing unit, a binarization unit and a segmentation unit; the smoothing unit filters frequency domain noise in the dam panoramic image through an interframe difference strategy to finish noise reduction processing; the binarization unit calculates the frequency of each gray value in the dam panoramic image subjected to noise reduction, calculates a binarization threshold value T according to the frequency, and performs binarization processing on the dam panoramic image subjected to noise reduction; the result output by the binarization unit is subjected to similar region segmentation by a segmentation unit.
As a preferable scheme of the dam monitoring and analyzing method based on the image recognition technology, the method comprises the following steps: the method comprises the following steps: the frequency f (x) of each gray value in the dam panoramic image after noise reduction is as follows:
the binarization threshold value T is as follows:
wherein, # xiRepresenting the grey values x in the image P (A)iP (a), represents the gray level histogram of the dam panoramic image after noise reduction, and m is the number of pixels of the dam panoramic image after noise reduction.
As a preferable scheme of the dam monitoring and analyzing method based on the image recognition technology, the method comprises the following steps: the similar region segmentation comprises the following steps: and performing downsampling through convolution and pooling, then performing upsampling through Deconv deconvolution to obtain a low-level feature map, performing fusion and upsampling on the low-level feature map, and outputting a segmentation result through a softmax function.
As a preferable scheme of the dam monitoring and analyzing method based on the image recognition technology, the method comprises the following steps: the dam crack detection model comprises a depth confidence network of a five-layer restricted Boltzmann machine and a layer of long-short term memory artificial neural network; the network weight omega of the dam crack detection model is as follows:
where δ is the step size, f (x, y) is the characteristic of the input, ω is0Is the initial network weight.
As a preferable scheme of the dam monitoring and analyzing method based on the image recognition technology, the method comprises the following steps: optimizing the population, namely calculating individual fitness Fit according to the network weight omega, and generating an initial population by using a chaotic genetic algorithm; selecting, crossing and mutating the initial population to obtain an evolved population; carrying out immigration operation on the evolved population by combining the fitness and setting an iteration condition; if the iteration condition is not met, recalculating the individual fitness; otherwise, outputting the result and obtaining the optimal value of the network weight; wherein the individual fitness Fit is as follows:
Fit＝1/ω。
as a preferable scheme of the dam monitoring and analyzing method based on the image recognition technology, the method comprises the following steps: generating the initial population comprises generating a network weight through floating point number coding, and then performing chaotic iteration j times on the network weight to further obtain the initial population.
As a preferred embodiment of the dam monitoring and analyzing system based on the image recognition technology, the dam monitoring and analyzing system comprises: the method comprises the following steps: the preprocessing module is used for preprocessing the acquired dam panoramic image; the characteristic extraction module is connected with the preprocessing module and used for extracting the characteristics of the preprocessed data; the characteristic fusion module is connected with the characteristic extraction module and used for establishing a dam crack detection model according to the characteristics extracted by the characteristic extraction module and optimizing the dam crack detection model; and identifying the cracks of the dam in real time through the optimized dam crack detection model.
As a preferred embodiment of the dam monitoring and analyzing system based on the image recognition technology, the dam monitoring and analyzing system comprises: the preprocessing module comprises a smoothing unit, a binarization unit and a segmentation unit; the smoothing unit is used for filtering frequency domain noise in the dam panoramic image; the binarization unit is connected with the smoothing unit and is used for carrying out binarization processing on the dam panoramic image subjected to noise reduction; and the segmentation unit is connected with the binarization unit and is used for performing similar region segmentation on the result output by the binarization unit.
The invention has the beneficial effects that: the method is based on artificial intelligence technologies such as machine vision, deep learning and the like, and can accurately detect the defects of the dam in the operation process by utilizing the computer to process, analyze and understand the images.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic structural diagram of a dam monitoring and analyzing system based on an image recognition technology according to a second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Also in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are only for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
The embodiment provides a dam monitoring and analyzing method based on an image recognition technology, which comprises the following steps:
s1: the collected dam panoramic image is preprocessed through the preprocessing module 100.
The preprocessing module 100 includes a smoothing unit 101, a binarization unit 102, and a segmentation unit 103;
(1) the smoothing unit 101 filters frequency domain noise in the dam panoramic image through an inter-frame difference strategy, and noise reduction is completed.
(2) The binarization unit 102 performs binarization processing on the dam panoramic image after noise reduction by calculating the frequency of each gray value in the dam panoramic image after noise reduction and calculating a binarization threshold value T according to the frequency.
The frequency f (x) of each gray value in the dam panoramic image after noise reduction is as follows:
the binarization threshold value T is as follows:
wherein, # xiRepresenting gray values x in an image PAiPA represents the gray level histogram of the dam panoramic image after noise reduction, and m is the number of pixels of the dam panoramic image after noise reduction.
(3) The result output by the binarization unit 102 is subjected to similar region segmentation by a segmentation unit 103.
The segmentation unit 103 is a U-net network, and the specific segmentation steps are as follows: firstly, downsampling is carried out through convolution and pooling, then upsampling is carried out through Deconv deconvolution, a low-level feature map is obtained, fusion and upsampling are carried out, and segmentation results are output through a softmax function.
S2: the feature extraction module 200 is used to extract features of the preprocessed image, and the features are sent to the feature fusion module 300.
In order to extract features accurately, the embodiment uses a local feature extraction algorithm-sift to extract features of the preprocessed image.
S3: the feature fusion module 300 establishes a dam crack detection model according to the features, and then optimizes the dam crack detection model based on the chaotic genetic algorithm.
The feature fusion module 300 establishes a dam crack detection model based on a neural network, wherein the dam crack detection model comprises a depth confidence network of a five-layer restricted Boltzmann machine and a layer of long-term and short-term memory artificial neural network;
the network weight omega of the dam crack detection model is as follows:
where δ is the step size, fx, y are the characteristics of the input, ω0Is the initial network weight.
Further, in order to make the identification precision of the dam crack detection model more accurate, the network weight of the dam crack detection model is optimized by combining the chaotic genetic algorithm, specifically:
(1) calculating individual fitness Fit according to the network weight omega, and generating an initial population by using a chaotic genetic algorithm;
the individual fitness Fit is as follows:
Fit＝1/ω。
and generating a network weight by floating point number coding, and performing chaotic iteration j times on the network weight to further obtain an initial population.
(2) Selecting, crossing and mutating the initial population to obtain an evolved population;
selection operation: and selecting individuals by adopting a tournament competition selection operator.
And step two, cross operation: and performing a crossover operation on the population generated by the selection according to the crossover probability of 75%.
③ variation operation: mutation operation is performed according to 2% mutation probability.
Performing mutation operation on the population generated after crossing according to the 2% mutation rate, and performing mutation according to the following formula:
wherein d isab' is parent dabThe rand of the filial generation generated after the variation is random number 0 or 1, wherein the rand represents that the individual variable changes towards the decreasing direction when the rand is 0, and the rand represents that the individual variable changes towards the increasing direction when the rand is 1; e is a random number in the interval (0, 1) for controlling the degree of variation.
(3) Carrying out immigration operation on the evolved population by combining fitness, and setting iteration conditions;
the iteration is stopped when the number of iterations reaches 500.
(4) If the iteration condition is not met, recalculating the individual fitness; otherwise, outputting the result and obtaining the optimal value of the network weight.
S4: and inputting the dam panoramic image into the optimized dam crack detection model, and identifying the cracks of the dam in real time.
In order to verify and explain the technical effects adopted in the method, the embodiment selects the traditional detection method and adopts the method to carry out comparison test, and compares the test results by means of scientific demonstration to verify the real effect of the method.
In order to verify that the method has higher identification precision for dam defects compared with the conventional detection method, in this embodiment, the conventional detection method and the method are respectively used for real-time defect detection comparison of the dam in the selected area, and the results are shown in the following table.
Table 1: dam crack identification accuracy.
| Conventional detection methods | Method for producing a composite material | |
| Region A | 80％ | 96.7％ |
| Region B | 76.3％ | 98.1％ |
| Region C | 77.8％ | 95.2％ |
As can be seen from the above table, the method can more accurately detect the defects of the dam compared to the conventional detection method.
Example 2
Referring to fig. 1, there is provided a second embodiment of the present invention, which is different from the first embodiment, in that it provides a dam monitoring and analyzing system based on image recognition technology, including,
the preprocessing module 100 is used for preprocessing the acquired dam panoramic image; the preprocessing module 100 includes a smoothing unit 101, a binarization unit 102, and a segmentation unit 103; the smoothing unit 101 is used for filtering frequency domain noise in the dam panoramic image; a binarization unit 102 connected with the smoothing unit 101 and used for performing binarization processing on the dam panoramic image subjected to noise reduction; the segmentation unit 103 is connected to the binarization unit 102 and is used for performing similar region segmentation on the result output by the binarization unit 102.
The feature extraction module 200 is connected to the preprocessing module 100, and is configured to perform feature extraction on the preprocessed data;
the feature fusion module 300 is connected with the feature extraction module 200, and is used for establishing a dam crack detection model according to the features extracted by the feature extraction module 200 and optimizing the dam crack detection model; and identifying the cracks of the dam in real time through the optimized dam crack detection model.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (9)
1. A dam monitoring and analyzing method based on an image recognition technology is characterized by comprising the following steps:
preprocessing the acquired dam panoramic image through a preprocessing module (100);
extracting the features of the preprocessed image by using a feature extraction module (200), and sending the features to a feature fusion module (300);
the characteristic fusion module (300) establishes a dam crack detection model according to the characteristics, and then optimizes the dam crack detection model based on a chaotic genetic algorithm;
and inputting the dam panoramic image into the optimized dam crack detection model, and identifying the cracks of the dam in real time.
2. The dam monitoring analysis method based on image recognition technology as claimed in claim 1, wherein the preprocessing module (100) comprises a smoothing unit (101), a binarization unit (102) and a segmentation unit (103);
the smoothing unit (101) filters frequency domain noise in the dam panoramic image through an interframe difference strategy to finish noise reduction processing;
the binarization unit (102) calculates the frequency of each gray value in the dam panoramic image subjected to noise reduction, calculates a binarization threshold value T according to the frequency, and performs binarization processing on the dam panoramic image subjected to noise reduction;
the result output by the binarization unit (102) is subjected to similar region segmentation by a segmentation unit (103).
3. The dam monitoring and analyzing method based on the image recognition technology as claimed in claim 2, comprising:
the frequency f (x) of each gray value in the dam panoramic image after noise reduction is as follows:
the binarization threshold value T is as follows:
wherein, # xiRepresenting the grey values x in the image P (A)iP (a), represents the gray level histogram of the dam panoramic image after noise reduction, and m is the number of pixels of the dam panoramic image after noise reduction.
4. The dam monitoring analysis method based on image recognition technology as claimed in claim 2, wherein the similar region segmentation comprises:
and performing down-sampling through convolution and pooling, then performing up-sampling through Deconv deconvolution to obtain a low-level feature map, performing fusion and up-sampling on the low-level feature map, and outputting a segmentation result through a softmax function.
5. The dam monitoring and analyzing method based on the image recognition technology as claimed in any one of claims 2 to 4, wherein the dam crack detection model comprises a depth confidence network of a five-layer restricted Boltzmann machine and a long-short term memory artificial neural network;
the network weight omega of the dam crack detection model is as follows:
where δ is the step size, f (x, y) is the characteristic of the input, ω is0Is the initial network weight.
6. The dam monitoring analysis method based on image recognition technology of claim 5, wherein the optimization includes,
calculating individual fitness Fit according to the network weight omega, and generating an initial population by using a chaotic genetic algorithm;
selecting, crossing and mutating the initial population to obtain an evolved population;
carrying out immigration operation on the evolved population by combining the fitness and setting an iteration condition;
if the iteration condition is not met, recalculating the individual fitness; otherwise, outputting the result and obtaining the optimal value of the network weight;
wherein the individual fitness Fit is as follows:
Fit＝1/ω。
7. the method of claim 6, wherein the generating of the initial population includes,
and generating a network weight through floating point number coding, and then performing chaotic iteration j times on the network weight to further obtain the initial population.
8. A dam monitoring and analyzing system based on image recognition technology is characterized by comprising:
the preprocessing module (100) is used for preprocessing the acquired dam panoramic image;
the characteristic extraction module (200) is connected with the preprocessing module (100) and is used for extracting the characteristics of the preprocessed data;
the characteristic fusion module (300) is connected with the characteristic extraction module (200) and used for establishing a dam crack detection model according to the characteristics extracted by the characteristic extraction module (200) and optimizing the dam crack detection model; and identifying the cracks of the dam in real time through the optimized dam crack detection model.
9. The dam monitoring analysis system based on image recognition technology as claimed in claim 8, wherein the preprocessing module (100) comprises a smoothing unit (101), a binarization unit (102) and a segmentation unit (103);
the smoothing unit (101) is used for filtering frequency domain noise in the dam panoramic image;
a binarization unit (102) connected with the smoothing unit (101) and used for carrying out binarization processing on the dam panoramic image subjected to noise reduction;
and a dividing unit (103) connected with the binarization unit (102) and used for carrying out similar region division on the result output by the binarization unit (102).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210099656.8A CN114596262A (en) | 2022-01-27 | 2022-01-27 | Dam monitoring and analyzing method and system based on image recognition technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210099656.8A CN114596262A (en) | 2022-01-27 | 2022-01-27 | Dam monitoring and analyzing method and system based on image recognition technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114596262A true CN114596262A (en) | 2022-06-07 |
Family
ID=81806183
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210099656.8A Pending CN114596262A (en) | 2022-01-27 | 2022-01-27 | Dam monitoring and analyzing method and system based on image recognition technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114596262A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116106430A (en) * | 2023-04-12 | 2023-05-12 | 中南大学 | Acoustic emission technology-based refractory material cracking diagnosis method for casting |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108764312A (en) * | 2018-05-17 | 2018-11-06 | 河海大学 | DS-based optimized multi-index dam defect image detection method |
| CN108764345A (en) * | 2018-05-30 | 2018-11-06 | 河海大学常州校区 | A kind of underwater Dam Crack detection method based on part and global clustering |
| CN108921201A (en) * | 2018-06-12 | 2018-11-30 | 河海大学 | Dam defect identification and classification method based on feature combination and CNN |
| CN109615616A (en) * | 2018-11-27 | 2019-04-12 | 北京联合大学 | A kind of crack identification method and system based on ABC-PCNN |
| KR20190095167A (en) * | 2018-02-05 | 2019-08-14 | 이철희 | Apparatus and method for focusing in camera |
| CN110147772A (en) * | 2019-05-23 | 2019-08-20 | 河海大学常州校区 | A kind of underwater dam surface crack recognition methods based on transfer learning |
| CN113139940A (en) * | 2021-04-21 | 2021-07-20 | 金华职业技术学院 | Steel plate surface crack detection algorithm and processing equipment based on computer vision |
-
2022
- 2022-01-27 CN CN202210099656.8A patent/CN114596262A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190095167A (en) * | 2018-02-05 | 2019-08-14 | 이철희 | Apparatus and method for focusing in camera |
| CN108764312A (en) * | 2018-05-17 | 2018-11-06 | 河海大学 | DS-based optimized multi-index dam defect image detection method |
| CN108764345A (en) * | 2018-05-30 | 2018-11-06 | 河海大学常州校区 | A kind of underwater Dam Crack detection method based on part and global clustering |
| CN108921201A (en) * | 2018-06-12 | 2018-11-30 | 河海大学 | Dam defect identification and classification method based on feature combination and CNN |
| CN109615616A (en) * | 2018-11-27 | 2019-04-12 | 北京联合大学 | A kind of crack identification method and system based on ABC-PCNN |
| CN110147772A (en) * | 2019-05-23 | 2019-08-20 | 河海大学常州校区 | A kind of underwater dam surface crack recognition methods based on transfer learning |
| CN113139940A (en) * | 2021-04-21 | 2021-07-20 | 金华职业技术学院 | Steel plate surface crack detection algorithm and processing equipment based on computer vision |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116106430A (en) * | 2023-04-12 | 2023-05-12 | 中南大学 | Acoustic emission technology-based refractory material cracking diagnosis method for casting |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2020155929A1 (en) | Method for determining rock mass integrity | |
| CN112560587A (en) | Dynamic early warning method and system for convolutional neural network slope crack change | |
| CN107462204A (en) | A kind of three-dimensional pavement nominal contour extracting method and system | |
| CN110909657A (en) | Method for identifying apparent tunnel disease image | |
| CN117495735B (en) | Automatic building elevation texture repairing method and system based on structure guidance | |
| CN113627068A (en) | Method and system for predicting well testing productivity of fracture-cavity type oil and gas reservoir | |
| CN114596262A (en) | Dam monitoring and analyzing method and system based on image recognition technology | |
| CN116091490A (en) | Lung nodule detection method based on YOLOv4-CA-CBAM-K-means++ -SIOU | |
| CN116563262A (en) | Building crack detection algorithm based on multiple modes | |
| CN110334775B (en) | Unmanned aerial vehicle line fault identification method and device based on width learning | |
| CN117876381A (en) | AI visual detection method and system for identifying and analyzing concrete structure cracks | |
| CN118072193A (en) | Dam crack detection method based on unmanned aerial vehicle image and deep learning | |
| CN115830302B (en) | Multi-scale feature extraction fusion power distribution network equipment positioning identification method | |
| CN111612907A (en) | Multidirectional repairing system and method for damaged ancient building column | |
| CN113379326B (en) | Power grid disaster emergency drilling management system based on deep neural network | |
| CN111126135B (en) | Feature self-adaptive pedestrian re-identification method based on unified division | |
| CN113781441B (en) | Grouting range optimization method applied to jointed rock mass tunnel excavation process | |
| CN116664573B (en) | Downhole drill rod number statistics method based on improved YOLOX | |
| CN117132896B (en) | Method for detecting and identifying building cracking | |
| Jing et al. | Complex Crack Segmentation and Quantitative Evaluation of Engineering Materials Based on Deep Learning Methods | |
| CN118071636B (en) | Intelligent acceptance method and system for building engineering construction | |
| CN113034502B (en) | Drainage pipeline defect redundancy removing method | |
| CN117763687A (en) | Building damage prediction system and method based on BIM model | |
| CN114511510A (en) | Method and device for automatically extracting ascending aorta image | |
| CN118447285A (en) | PE gas pipeline joint defect identification method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |