CN113066072B - Method and system for detecting microcrack defects of guide blade of aero-engine - Google Patents
Method and system for detecting microcrack defects of guide blade of aero-engine Download PDFInfo
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Abstract
The invention relates to a method and a system for detecting microcrack defects of guide blades of an aeroengine, wherein the detection method comprises the following steps: acquiring a DR detection image of a part to be detected of the guide vane of the aero-engine through a DR detection system; obtaining an original gray matrix according to the DR detection image; according to the DR detection image, a gray level distribution histogram and a signal to noise ratio of the DR detection image are obtained; obtaining an enhanced gray matrix according to the gray distribution histogram, the signal-to-noise ratio and the DR detection image; masking the original gray matrix to obtain a low-frequency gray matrix; and obtaining a high-frequency gray level detail information matrix according to the enhanced gray level matrix and the low-frequency gray level matrix, wherein the high-frequency gray level detail information matrix represents the microcrack defect condition of the part to be tested of the guide vane of the aeroengine. The effect of increasing the contrast of the DR detection image is achieved, the detail information defect outline in the image is highlighted, and the defect of microcracks of the guide vane of the aeroengine can be clearly identified.
Description
Technical Field
The invention relates to the technical field of image enhancement, in particular to a method and a system for detecting microcrack defects of guide blades of an aeroengine.
Background
In aircraft engines, the blades are one of the key components that power the engine, and their primary function is to compress the air in the engine and the operating conditions will directly affect the efficiency, safety and reliability of the engine. For engine turbine blades, guide vanes and rotor blades are critical components that perform functional transformations within the engine. In the high-speed running state of the engine, the blades are subjected to complex loads and tensile stress and torsional stress caused by high-speed rotation, so that the quality detection of the guide blades becomes a serious weight in the quality evaluation of the aeroengine.
The safety of the guide vane is a critical feature, which concerns the safety of the engine and the flight. After the casting of the guide vane is completed, nondestructive testing is required, and usually for the tiny defects of the guide vane, ultrasonic testing and magnetic powder testing are often adopted, and the detected signals are analyzed, so that defect information can be further obtained. It is necessary to visually see the shape and characteristics of the minute defect, acquire DR defect image using DR (Digital Radiography ) detection system, and add image processing at the back end.
DR is an emerging imaging technology for industrial non-destructive testing that is used to produce high quality DR digital images that are informative. The obtained DR digital image can be optimized and perfected by utilizing the digital image processing technology, so that a better observation effect can be achieved, and engineers can find out tiny defects of workpieces hidden in the DR image in time conveniently. The DR image enhancement methods are mainly divided into two main categories: enhancing the DR image contrast and highlighting detail information of the DR image. For DR images with low contrast and less detail information, target detail enhancement cannot be effectively performed by using a traditional histogram enhancement method. In digital image processing technology, there is an algorithm that limits the contrast adaptive histogram equalization, where the clipping threshold is adjustable, but the manual adjustment does not reach the optimal adjustment parameters.
Based on the above problems, a new detection method is needed to improve the detection accuracy.
Disclosure of Invention
The invention aims to provide a method and a system for detecting microcrack defects of an aircraft engine guide blade, which can clearly identify the defects of microcracks of the aircraft engine guide blade.
In order to achieve the above object, the present invention provides the following solutions:
an aeroengine guide vane microcrack defect detection method, comprising:
acquiring a DR detection image of a part to be detected of the guide vane of the aero-engine through a DR detection system;
obtaining an original gray matrix according to the DR detection image;
according to the DR detection image, a gray level distribution histogram and a signal to noise ratio of the DR detection image are obtained;
obtaining an enhanced gray matrix according to the gray distribution histogram, the signal-to-noise ratio and the DR detection image;
performing mask processing on the original gray matrix to obtain a low-frequency gray matrix;
and obtaining a high-frequency gray level detail information matrix according to the enhanced gray level matrix and the low-frequency gray level matrix, wherein the high-frequency gray level detail information matrix represents the microcrack defect condition of the part to be tested of the aero-engine guide blade.
Optionally, the DR detection system comprises a digital radiography system and a digital flat panel detector imaging system;
transmitting X-rays to a part to be tested of the guide vane of the aero-engine through the digital ray system;
the digital flat panel detector imaging system acquires X-rays reflected by a part to be detected of the guide blade of the aeroengine and converts the X-rays into DR detection images.
Optionally, the obtaining an enhanced gray matrix according to the gray distribution histogram, the signal-to-noise ratio, and the DR detection image specifically includes:
determining an image segmentation mode and a gray scale mapping range according to the distribution characteristics of the gray scale distribution histogram;
according to the gray distribution histogram and the signal to noise ratio, an optimal clipping threshold value is obtained by adopting a particle swarm optimization algorithm;
and processing the DR detection image by adopting a particle swarm optimization algorithm and an image enhancement algorithm for limiting contrast self-adaptive histogram equalization according to the image segmentation mode, the gray scale mapping range and the optimal clipping threshold value to obtain an enhanced gray scale matrix.
Optionally, the gray-scale mapping range is [ a minimum gray-scale value of the DR detection image, a maximum gray-scale value of the DR detection image ].
Optionally, the masking processing is performed on the original gray matrix to obtain a low-frequency gray matrix, which specifically includes:
and carrying out fuzzy processing on the original gray matrix by adopting Gaussian fuzzy processing based on a spatial mean filter to obtain a low-frequency gray matrix.
Optionally, the obtaining a high-frequency gray scale detail information matrix according to the enhanced gray scale matrix and the low-frequency gray scale matrix specifically includes:
obtaining a high-frequency gray level detail information matrix according to the formula D=2B-C;
wherein D is a high-frequency gray scale detail information matrix, B is a preliminary enhanced gray scale matrix, and C is a low-frequency gray scale matrix.
Optionally, the aeroengine guide vane microcrack defect detection method further comprises the following steps:
and carrying out Gaussian mask circulation processing on the high-frequency gray level detail information matrix for a plurality of times to obtain an enhanced high-frequency gray level detail information matrix.
Optionally, the performing gaussian mask circulation processing on the high-frequency gray scale detail information matrix for multiple times to obtain an enhanced high-frequency gray scale detail information matrix specifically includes:
for the ith Gaussian mask cycle process, gray is calculated according to the formula i ＝2Gray i-1 -Gray g-(i-1) Obtaining a high-frequency gray level detail information matrix after the ith mask cycle treatment;
wherein i is the number of cycles, gray i Gray is a high-frequency Gray scale detail information matrix processed by the ith Gaussian mask i-1 Is a high-frequency Gray scale detail information matrix processed by the i-1 th Gaussian mask, gray g-(i-1) To pair Gray i-1 High-frequency Gray detail information matrix after Gaussian filtering and Gray 0 Gray is an initial high-frequency Gray detail information matrix obtained according to the enhanced Gray matrix and the low-frequency Gray matrix g-0 To pair Gray 0 And carrying out Gaussian filtering on the high-frequency gray level detail information matrix.
Optionally, the number of times of cyclic processing of performing multiple times of gaussian mask cyclic processing on the high-frequency gray scale detail information matrix is 2-3 times.
In order to achieve the above purpose, the present invention also provides the following solutions:
an aircraft engine guide vane microcrack defect detection system, the detection system comprising:
the DR detection system is used for detecting the part to be detected of the guide blade of the aeroengine to obtain a DR detection image;
the computing unit is connected with the DR detection system and is used for computing an original gray matrix, a gray distribution histogram and a signal-to-noise ratio of the DR detection image;
the enhancement unit is connected with the calculation unit and is used for enhancing the DR detection image according to the gray distribution histogram and the signal to noise ratio to obtain an enhanced gray matrix;
the mask unit is connected with the calculation unit and is used for performing mask processing on the original gray matrix to obtain a low-frequency gray matrix;
the detail processing unit is respectively connected with the enhancement unit and the mask unit and is used for obtaining a high-frequency gray level detail information matrix according to the enhancement gray level matrix and the low-frequency gray level matrix, and the high-frequency gray level detail information matrix represents the microcrack defect condition of the part to be tested of the guide vane of the aeroengine.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the DR detection image of the part to be detected of the guide vane of the aeroengine is obtained through the DR detection system, the original gray matrix, the gray distribution histogram and the signal to noise ratio of the DR detection image are obtained, the high-frequency gray detail information matrix is obtained according to the original gray matrix, the gray distribution histogram and the signal to noise ratio, the high-frequency gray detail information matrix represents the microcrack defect condition, the effect of increasing the contrast of the DR detection image is achieved, the detail information defect outline in the image is highlighted, and the microcrack defect of the guide vane of the aeroengine can be clearly identified.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an overall flow chart of a method for detecting microcrack defects in a guide vane of an aircraft engine according to the present invention;
FIG. 2 is a flow chart for obtaining an enhanced gray matrix;
FIG. 3 is a schematic block diagram of an aircraft engine guide vane microcrack defect detection system according to the present invention.
Symbol description:
DR detection system-1, calculation unit-2, enhancement unit-3, mask unit-4, detail processing unit-5.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method and a system for detecting microcrack defects of an aeroengine guide blade, which are characterized in that a DR detection image of a part to be detected of the aeroengine guide blade is obtained through a DR detection system, an original gray matrix, a gray distribution histogram and a signal-to-noise ratio of the DR detection image are obtained, a high-frequency gray detail information matrix is obtained according to the original gray matrix, the gray distribution histogram and the signal-to-noise ratio, the high-frequency gray detail information matrix represents microcrack defect conditions, the effect of increasing the contrast of the DR detection image is achieved, the detail information defect outline in the image is highlighted, and the microcrack defects of the aeroengine guide blade can be clearly identified.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in FIG. 1, the method for detecting the microcrack defect of the guide vane of the aeroengine comprises the following steps:
s1: acquiring a DR detection image of a part to be detected of the guide vane of the aero-engine through a DR detection system;
s2: obtaining an original gray matrix according to the DR detection image;
s3: and obtaining a gray level distribution histogram and a signal to noise ratio of the DR detection image according to the DR detection image.
In order to improve detection accuracy, in the method for detecting microcrack defects of the guide vane of the aeroengine, when the gray distribution histogram is obtained, the DR detection image of each frame is converted into a format image of a cross-platform computer image visual library, a corresponding time stamp is analyzed, and each pixel point of the format image is identified to obtain the gray distribution histogram.
S4: obtaining an enhanced gray matrix according to the gray distribution histogram, the signal-to-noise ratio and the DR detection image;
s5: performing mask processing on the original gray matrix to obtain a low-frequency gray matrix;
s6: and obtaining a high-frequency gray level detail information matrix according to the enhanced gray level matrix and the low-frequency gray level matrix, wherein the high-frequency gray level detail information matrix represents the microcrack defect condition of the part to be tested of the aero-engine guide blade.
Wherein the DR detection system comprises a digital ray system and a digital flat panel detector imaging system;
transmitting X-rays to a part to be tested of the guide vane of the aero-engine through the digital ray system;
the digital flat panel detector imaging system acquires X-rays reflected by a part to be detected of the guide blade of the aeroengine and converts the X-rays into DR detection images.
The digital ray system and the digital flat panel detector imaging system can acquire high-quality DR digital images, the acquired DR digital images have sufficient information quantity, sufficient guarantee is provided for subsequent image processing, and the defects of microcracks of the guide vane can be clearly identified.
Further, as shown in fig. 2, S4: obtaining an enhanced gray matrix according to the gray distribution histogram, the signal-to-noise ratio and the DR detection image, wherein the enhanced gray matrix specifically comprises:
s41: and determining an image segmentation mode and a gray level mapping range according to the distribution characteristics of the gray level distribution histogram.
The image segmentation mode is generally m×n (m=2, 4,8, 16; n=2, 4,8, 16, etc.), and the optimal parameter selection is specifically performed according to the image characteristics. The gray-scale mapping range is [ the minimum gray-scale value of the DR detection image, the maximum gray-scale value of the DR detection image ].
S42: and obtaining an optimal clipping threshold value by adopting a particle swarm optimization algorithm according to the gray level distribution histogram and the signal to noise ratio. Specifically:
the ith particle of the particle swarm optimization algorithm is denoted as X i ＝(x i1 ,x i2 ,...,x iD ) The best position it has undergone (with the best fitness value) is noted as P i ＝(p i1 ,p i2 ,...,p iD ). The index number at the best position that all microparticles of the population have undergone is denoted by g, i.e. p g . V for the velocity of the particles i i ＝(v i1 ,v i2 ,...,v iD ) And (3) representing. For each generation, its D-th dimension (1. Ltoreq.d. Ltoreq.D) varies according to the following formula:
V id ＝w*V id +c 1 *rand()*(p id -x id )+c 2 *Rand*(p gd -x id )；
x id ＝x id +V id ；
wherein w is inertial weight, c1, c2 are acceleration constants, and Rand () are two of 0,1]Random values that vary within a range. Taking as input the image signal-to-noise ratio: p (P) i An optimal output g is obtained through a particle swarm algorithm, which is equivalent to an optimal clipping threshold in a limiting contrast adaptive histogram equalization algorithm. In the present embodiment, the optimum clipping threshold cliplimit=3.89.
S43: and processing the DR detection image by adopting a particle swarm optimization algorithm and an image enhancement algorithm for limiting contrast self-adaptive histogram equalization according to the image segmentation mode, the gray scale mapping range and the optimal clipping threshold value to obtain an enhanced gray scale matrix.
The invention introduces an optimizing algorithm: particle swarm optimization (Particle Swarm Optimization, PSO), also known as particle swarm optimization, is to consider each individual as a particle (dot) of no volume in a D-dimensional search space, flying at a speed in the search space that is dynamically adjusted based on its own flight experience and the flight experience of the companion. And searching an optimal clipping threshold Cliplimit, taking an image signal-to-noise ratio (SNR, signal Noise Ratio) as an optimizing basis, and finally achieving an optimal effect of tuning.
The optimal clipping threshold value is obtained through a particle swarm optimization algorithm, the optimal image contrast enhancement effect is achieved, after the detection image is processed through a limited contrast self-adaptive histogram equalization algorithm, a high-frequency image gray information matrix is obtained through Gaussian blur processing, and then the high-frequency image gray information matrix is processed through Gaussian mask circulation for multiple times, so that the image contrast is further improved, the image detail information is highlighted, the effect of increasing the image contrast is achieved, and the detail information defect outline in the image is highlighted.
Further, S5: masking the original gray matrix to obtain a low-frequency gray matrix, which comprises the following steps: and carrying out fuzzy processing on the original gray matrix by adopting Gaussian fuzzy processing based on a spatial mean filter to obtain a low-frequency gray matrix.
Preferably, S6: obtaining a high-frequency gray level detail information matrix according to the enhanced gray level matrix and the low-frequency gray level matrix, wherein the method specifically comprises the following steps of:
obtaining a high-frequency gray level detail information matrix according to the formula D=2B-C;
wherein D is a high-frequency gray scale detail information matrix, B is a preliminary enhanced gray scale matrix, and C is a low-frequency gray scale matrix.
Further, the method for detecting the microcrack defect of the guide vane of the aeroengine further comprises the following steps:
s7: and carrying out Gaussian mask circulation processing on the high-frequency gray level detail information matrix for a plurality of times to obtain an enhanced high-frequency gray level detail information matrix. The method specifically comprises the following steps:
for the ith Gaussian mask cycle process, gray is calculated according to the formula i ＝2Gray i-1 -Gray g-(i-1) Obtaining a high-frequency gray level detail information matrix after the ith mask cycle treatment;
wherein i is the number of cycles, gray i Gray is a high-frequency Gray scale detail information matrix processed by the ith Gaussian mask i-1 Is a high-frequency Gray scale detail information matrix processed by the i-1 th Gaussian mask, gray g-(i-1) To pair Gray i-1 High-frequency Gray detail information matrix after Gaussian filtering and Gray 0 Gray is an initial high-frequency Gray detail information matrix obtained according to the enhanced Gray matrix and the low-frequency Gray matrix g-0 To pair Gray 0 And carrying out Gaussian filtering on the high-frequency gray level detail information matrix.
Preferably, the number of cycles i is 2 or 3.
The specific circulation steps are as follows:
wherein Gray (D) Gray is an initial high-frequency Gray detail information matrix obtained according to the enhanced Gray matrix and the low-frequency Gray matrix (g-D) To pair Gray D High-frequency Gray detail information matrix after Gaussian filtering and Gray 1 For the high-frequency Gray detail information matrix after the first Gaussian mask processing, gray g-1 Gray is processed for Gaussian filtering 1 High frequency Gray detail information matrix of (a), and the same, gray 2 For the high-frequency Gray detail information matrix after the second Gaussian mask processing, gray 3 And the high-frequency gray scale detail information matrix after the third Gaussian mask processing. The number of gaussian mask loop processing is determined according to a specific image. When the guide vane of the aeroengine is subjected to Gaussian mask circulation treatment, the optimal times are 2 times, so that the effect of highlighting detailed information is achieved, and the image contrast is improved.
The final result of target image processing can accurately identify the tiny defects on the guide vane, and is convenient for a nondestructive testing engineer to observe, so that the image processing method is feasible and effective for the detection image processing of the microcrack DR of the guide vane of the aeroengine.
In addition, the invention also provides a system for detecting the microcrack defects of the guide vane of the aeroengine, which can clearly identify the defects of the microcrack of the guide vane of the aeroengine.
As shown in fig. 3, the microcrack defect detection system of the guide vane of the aeroengine of the present invention comprises a DR detection system 1, a calculation unit 2, a reinforcement unit 3, a mask unit 4 and a detail processing unit 5.
Specifically, the DR detection system 1 is configured to detect a portion to be detected of a guide vane of an aero-engine, so as to obtain a DR detection image;
the computing unit 2 is connected with the DR detection system 1, and the computing unit 2 is used for computing an original gray matrix, a gray distribution histogram and a signal-to-noise ratio of the DR detection image;
the enhancement unit 3 is connected with the calculation unit 2, and the enhancement unit 3 is used for enhancing the DR detection image according to the gray distribution histogram and the signal to noise ratio to obtain an enhanced gray matrix;
the mask unit 4 is connected with the computing unit 2, and is used for performing mask processing on the original gray matrix to obtain a low-frequency gray matrix;
the detail processing unit 5 is respectively connected with the enhancement unit 3 and the mask unit 4, and the detail processing unit 5 is used for obtaining a high-frequency gray level detail information matrix according to the enhancement gray level matrix and the low-frequency gray level matrix, and the high-frequency gray level detail information matrix represents the microcrack defect condition of the part to be tested of the guide vane of the aeroengine.
Further, the DR detection system includes a digital radiography system and a digital flat panel detector imaging system;
the digital ray system is used for emitting X rays to a part to be tested of the aero-engine guide blade;
the digital flat panel detector imaging system is used for acquiring X-rays reflected by a part to be tested of the aeroengine guide blade and converting the X-rays into DR detection images.
Specifically, the reinforcement unit 3 includes: the device comprises a segmentation mode determining module, a cutting threshold determining module and a processing module;
the segmentation mode determining module is used for determining an image segmentation mode and a gray scale mapping range according to the distribution characteristics of the gray scale distribution histogram;
the clipping threshold determining module is used for obtaining an optimal clipping threshold by adopting a particle swarm optimization algorithm according to the gray distribution histogram and the signal to noise ratio;
the processing module is used for processing the DR detection image by adopting a particle swarm optimization algorithm and an image enhancement algorithm for limiting the contrast self-adaptive histogram equalization according to the image segmentation mode, the gray scale mapping range and the optimal clipping threshold value to obtain an enhanced gray scale matrix.
The invention has the following beneficial effects:
(1) Parameters in the adaptive histogram equalization algorithm for limiting the line contrast can be flexibly and accurately adjusted, so that the image contrast is improved;
(2) Obtaining an optimal clipping threshold value by using a particle swarm optimization algorithm, and achieving an optimal image contrast enhancement effect;
(3) After the target image is processed by a limited contrast self-adaptive histogram equalization algorithm, the target image and an original image subjected to Gaussian blur processing are subjected to high-frequency image gray information extraction, so that image gray detail information is obtained;
(4) And (3) carrying out Gaussian mask circulation on the image generated in the step (3) for a plurality of times. The Gaussian mask circulation times can be selected according to the processing result of each Gaussian mask so as to achieve the effects of further improving the image contrast and highlighting the image detail information;
(5) The whole processing process achieves the effect of increasing the contrast of the image and highlights the detail information defect outline in the image.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (7)
1. The method for detecting the microcrack defects of the guide vane of the aeroengine is characterized by comprising the following steps of:
acquiring a DR detection image of a part to be detected of the guide vane of the aeroengine by a digital radiography DR detection system;
obtaining an original gray matrix according to the DR detection image;
according to the DR detection image, a gray level distribution histogram and a signal to noise ratio of the DR detection image are obtained;
obtaining an enhanced gray matrix according to the gray distribution histogram, the signal-to-noise ratio and the DR detection image, wherein the enhanced gray matrix specifically comprises: determining an image segmentation mode and a gray scale mapping range according to the distribution characteristics of the gray scale distribution histogram; according to the gray distribution histogram and the signal to noise ratio, an optimal clipping threshold value is obtained by adopting a particle swarm optimization algorithm; according to the image segmentation mode, the gray level mapping range and the optimal clipping threshold, processing the DR detection image by adopting a particle swarm optimization algorithm and an image enhancement algorithm for limiting contrast self-adaptive histogram equalization to obtain an enhanced gray level matrix;
masking the original gray matrix to obtain a low-frequency gray matrix, which comprises the following steps: carrying out fuzzy processing on the original gray matrix by adopting Gaussian fuzzy processing based on a spatial mean filter to obtain a low-frequency gray matrix;
obtaining a high-frequency gray level detail information matrix according to the enhanced gray level matrix and the low-frequency gray level matrix, wherein the method specifically comprises the following steps of: obtaining a high-frequency gray level detail information matrix according to the formula D=2B-C; wherein D is a high-frequency gray scale detail information matrix, B is a preliminary enhanced gray scale matrix, and C is a low-frequency gray scale matrix; and the high-frequency gray level detail information matrix represents the microcrack defect condition of the part to be tested of the guide vane of the aeroengine.
2. The method for detecting microcrack defects of an aircraft engine guide vane of claim 1 wherein said DR detection system comprises a digital radiography system and a digital flat panel detector imaging system;
transmitting X-rays to a part to be tested of the guide vane of the aero-engine through the digital ray system;
the digital flat panel detector imaging system acquires X-rays reflected by a part to be detected of the guide blade of the aeroengine and converts the X-rays into DR detection images.
3. The method of claim 1, wherein the gray scale mapping range is [ minimum gray scale value of the DR detection image, maximum gray scale value of the DR detection image ].
4. The aircraft engine guide vane microcrack defect detection method of any one of claims 1-3, further comprising:
and carrying out Gaussian mask circulation processing on the high-frequency gray level detail information matrix for a plurality of times to obtain an enhanced high-frequency gray level detail information matrix.
5. The method for detecting micro-crack defects of an aircraft engine guide vane according to claim 4, wherein the performing gaussian mask cycle processing on the high-frequency gray scale detail information matrix for a plurality of times to obtain an enhanced high-frequency gray scale detail information matrix specifically comprises:
for the ith Gaussian mask cycle process, gray is calculated according to the formula i ＝2Gray i-1 -Gray g-(i-1) Obtaining a high-frequency gray level detail information matrix after the ith mask cycle treatment;
wherein i is the number of cycles, gray i Gray is a high-frequency Gray scale detail information matrix processed by the ith Gaussian mask i-1 Is a high-frequency Gray scale detail information matrix processed by the i-1 th Gaussian mask, gray g-(i-1) To pair Gray i-1 High-frequency Gray detail information matrix after Gaussian filtering and Gray 0 Gray is an initial high-frequency Gray detail information matrix obtained according to the enhanced Gray matrix and the low-frequency Gray matrix g-0 To pair Gray 0 And carrying out Gaussian filtering on the high-frequency gray level detail information matrix.
6. The method for detecting micro-crack defects of a guide vane of an aircraft engine according to claim 4, wherein the number of cyclic processes for performing a plurality of gaussian mask cyclic processes on the high-frequency gray scale detail information matrix is 2 to 3.
7. An aircraft engine guide vane microcrack defect detection system, the detection system comprising:
the DR detection system is used for detecting the part to be detected of the guide blade of the aeroengine to obtain a DR detection image;
the computing unit is connected with the DR detection system and is used for computing an original gray matrix, a gray distribution histogram and a signal-to-noise ratio of the DR detection image;
the enhancement unit is connected with the calculation unit and is used for enhancing the DR detection image according to the gray distribution histogram and the signal to noise ratio to obtain an enhanced gray matrix, and specifically comprises the following steps: determining an image segmentation mode and a gray scale mapping range according to the distribution characteristics of the gray scale distribution histogram; according to the gray distribution histogram and the signal to noise ratio, an optimal clipping threshold value is obtained by adopting a particle swarm optimization algorithm; according to the image segmentation mode, the gray level mapping range and the optimal clipping threshold, processing the DR detection image by adopting a particle swarm optimization algorithm and an image enhancement algorithm for limiting contrast self-adaptive histogram equalization to obtain an enhanced gray level matrix;
the mask unit is connected with the calculation unit and is used for carrying out mask processing on the original gray matrix to obtain a low-frequency gray matrix, and specifically comprises the following steps: carrying out fuzzy processing on the original gray matrix by adopting Gaussian fuzzy processing based on a spatial mean filter to obtain a low-frequency gray matrix;
the detail processing unit is respectively connected with the enhancement unit and the mask unit and is used for obtaining a high-frequency gray level detail information matrix according to the enhancement gray level matrix and the low-frequency gray level matrix, and specifically comprises the following steps: obtaining a high-frequency gray level detail information matrix according to the formula D=2B-C; wherein D is a high-frequency gray scale detail information matrix, B is a preliminary enhanced gray scale matrix, and C is a low-frequency gray scale matrix; and the high-frequency gray level detail information matrix represents the microcrack defect condition of the part to be tested of the guide vane of the aeroengine.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134211A (en) * | 2014-07-23 | 2014-11-05 | 西安工程大学 | Band steel defect detection method based on multi-resolution filtering of Gaussian filter |
| CN106204478A (en) * | 2016-07-06 | 2016-12-07 | 电子科技大学 | The magneto optic images based on background noise feature space strengthens algorithm |
| CN109727233A (en) * | 2018-12-18 | 2019-05-07 | 武汉精立电子技术有限公司 | A kind of LCD defect inspection method |
| CN110118775A (en) * | 2019-05-10 | 2019-08-13 | 重庆交通大学 | Plantmix's cement stabilized macadam aggregate forms rapid detection method |
| CN110333267A (en) * | 2019-07-01 | 2019-10-15 | 武汉科技大学 | One kind being based on RSBSS Infrared Non-destructive Testing thermal imaging imperfection image processing method and system |
| CN111145144A (en) * | 2019-12-06 | 2020-05-12 | 西安理工大学 | Method for evaluating surface roughness of honing cylinder sleeve |
| CN112150437A (en) * | 2020-09-23 | 2020-12-29 | 南昌航空大学 | DR (digital radiography) detection image processing method for crack defects of diffuser in laser additive manufacturing |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106023200B (en) * | 2016-05-19 | 2019-06-28 | 四川大学 | A kind of x-ray chest radiograph image rib cage suppressing method based on Poisson model |
-
2021
- 2021-04-08 CN CN202110379197.4A patent/CN113066072B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134211A (en) * | 2014-07-23 | 2014-11-05 | 西安工程大学 | Band steel defect detection method based on multi-resolution filtering of Gaussian filter |
| CN106204478A (en) * | 2016-07-06 | 2016-12-07 | 电子科技大学 | The magneto optic images based on background noise feature space strengthens algorithm |
| CN109727233A (en) * | 2018-12-18 | 2019-05-07 | 武汉精立电子技术有限公司 | A kind of LCD defect inspection method |
| CN110118775A (en) * | 2019-05-10 | 2019-08-13 | 重庆交通大学 | Plantmix's cement stabilized macadam aggregate forms rapid detection method |
| CN110333267A (en) * | 2019-07-01 | 2019-10-15 | 武汉科技大学 | One kind being based on RSBSS Infrared Non-destructive Testing thermal imaging imperfection image processing method and system |
| CN111145144A (en) * | 2019-12-06 | 2020-05-12 | 西安理工大学 | Method for evaluating surface roughness of honing cylinder sleeve |
| CN112150437A (en) * | 2020-09-23 | 2020-12-29 | 南昌航空大学 | DR (digital radiography) detection image processing method for crack defects of diffuser in laser additive manufacturing |
Non-Patent Citations (2)
| Title |
|---|
| 2A50锻铝三点弯曲试验与高分辨率CT成像分析;敖波;《材料工程》(第05期);全文 * |
| Contrast Enhancement for the detection of Stroke Signs in CT using PCA Based Image Fusion;Jeena R S;《College of Engineering Trivandrum》;全文 * |
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