CN110057745B - Infrared detection method for corrosion condition of metal component - Google Patents
Infrared detection method for corrosion condition of metal component Download PDFInfo
- Publication number
- CN110057745B CN110057745B CN201910316762.5A CN201910316762A CN110057745B CN 110057745 B CN110057745 B CN 110057745B CN 201910316762 A CN201910316762 A CN 201910316762A CN 110057745 B CN110057745 B CN 110057745B
- Authority
- CN
- China
- Prior art keywords
- temperature
- corrosion
- area
- layer
- metal
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N17/00—Investigating resistance of materials to the weather, to corrosion, or to light
- G01N17/006—Investigating resistance of materials to the weather, to corrosion, or to light of metals
Abstract
The invention belongs to the technical field of infrared thermographic nondestructive testing, and particularly discloses an infrared detection method for corrosion conditions of metal components. Acquiring an infrared image of a metal component, carrying out filtering and denoising treatment, and then setting a threshold value to obtain a binary image; marking an eroded area and an unetched area, and drawing the outline of the eroded area; extracting a mask of the corroded area in the binary image according to the outline of the corroded area, and calculating the temperature of the corroded area in the infrared image by using the mask; and inputting the temperature of the corrosion area into a temperature corrosion depth conversion model to obtain the corrosion depth of the metal member. According to the infrared detection method for the corrosion condition of the metal member, provided by the invention, the corrosion condition of the metal member can be specifically described from three aspects including a corrosion position, a corrosion area and a corrosion depth according to the temperature corrosion depth conversion model by only processing the infrared image, so that the qualitative and quantitative analysis of the corrosion condition of the metal member is realized.
Description
Technical Field
The invention belongs to the technical field of infrared thermographic nondestructive testing, and particularly relates to an infrared detection method for corrosion conditions of metal components.
Background
Corrosion is a chemical or electrochemical reaction between a metal or metal alloy and its environment, leading to a deterioration of the material and its properties. If not discovered in a timely manner, corrosion can lead to serious metal failure and can result in economic and safety concerns.
The infrared nondestructive testing technology is a nondestructive testing technology with ideal effect, and the principle is that a test piece to be tested is heated through a heat source, and real-time image signals of the surface temperature of the test piece are acquired by adopting infrared thermal imaging equipment. In the heat conduction process, when the test piece is internally corroded, the heat conduction performance of the material can be changed, the surface temperature of the test piece is unevenly distributed, and the corrosion information inside the test piece can be judged by processing the collected temperature signal.
At present, the infrared nondestructive testing technology is widely applied to the industries of electronic industry, mechanical manufacturing, pipeline testing, aerospace and the like, but the actual application is mainly limited to determining the corrosion position, and the quantitative analysis of the corrosion condition lacks a powerful theoretical basis. Although some numerical simulation analysis theories for the corrosion degree exist at present, the method is still in a preliminary development stage, and due to the fact that the calculation is complex and the programming workload is large, an image processing algorithm for specific corrosion conditions of metal components is still difficult to give.
Disclosure of Invention
In view of the above-mentioned shortcomings and/or needs of the prior art, the present invention provides an infrared detection method for detecting corrosion of a metal member, wherein the corrosion depth of the metal member can be obtained according to the temperature of a corrosion region on an infrared image by a temperature corrosion depth conversion model, and thus the method is particularly suitable for applications such as detecting corrosion of the metal member.
In order to achieve the purpose, the invention provides an infrared detection method for the corrosion condition of a metal component, which comprises the following steps:
s1, acquiring an infrared image of the metal component, carrying out filtering and denoising treatment, and then setting a threshold value to acquire a binary image of the infrared image;
s2, marking a corroded area and an unetched area according to the connectivity of each pixel and the adjacent pixel in the binary image, and drawing the outline of the corroded area;
s3, extracting a mask of the corroded area in the binary image according to the outline of the corroded area, and calculating the temperature of the corroded area in the infrared image by using the mask;
s4, inputting the temperature of the corrosion area into a temperature corrosion depth conversion model to obtain the corrosion depth of the metal component.
Further preferably, in step S1, the filtering and denoising process is performed by using an edge preserving filtering method.
Further preferably, in step S1, an Otsu binarization algorithm or a maximum normalized temperature difference method is used to obtain a binarized image of the infrared image.
Further preferably, in step S3, the temperature of the erosion area includes a temperature of each pixel in the erosion area, an average temperature of the erosion area, or a maximum temperature of the erosion area.
As a further preference, in step S4, the implicit function of the corrosion depth in the temperature corrosion depth conversion model is:
in the formula, AkCoefficient k for Fourier expansion, βkIs the k characteristic value, BkCoefficient k of Fourier expansionkIs the kth characteristic value which is a function of the etch depth, tau is the heating time, a1Thermal diffusivity for corrosion layers, a2Thermal diffusivity of an unetched layer, thickness of a metal member, t0Is the initial temperature of the metal member, tfTo heat the temperature, λ1To the thermal conductivity of the metal of the corrosion layer, lambda2The thermal conductivity of the metal of the non-corroded layer, h is the surface heat transfer coefficient of the metal member, TbTemperature of non-corroded area, T1Is the temperature of the corrosion region.
As a further preferred, in step S4, the method for constructing the temperature corrosion depth conversion model includes the following sub-steps:
s41, constructing a model of the non-corroded area, and obtaining the temperature T of the non-corroded areabComprises the following steps:
s42, constructing a model of the corrosion area with Dep corrosion depth, and obtaining the temperature T of the corrosion area1Comprises the following steps:
s43 obtains an implicit function of the etch depth Dep according to the formula (two) and the formula (three):
further preferably, in step S2, the method further includes calculating an area of the erosion area according to the contour of the erosion area.
Generally, compared with the prior art, the above technical solution conceived by the present invention mainly has the following technical advantages:
1. the invention combines the technologies of infrared imaging, signal detection, image processing and the like, and provides an infrared detection method for the corrosion condition of a metal component, wherein the corrosion position and the corrosion depth of the metal component can be obtained by processing an infrared image and converting a model according to the temperature corrosion depth, so that the corrosion can be qualitatively and quantitatively analyzed, the requirements of actual production detection can be basically met, and the infrared detection method has the advantages of no damage, rapidness, intuition, non-contact, large one-time detection area, simple implementation and the like, and is suitable for external field and online detection;
2. particularly, the corrosion depth, the average corrosion depth or the maximum corrosion depth of each point in the corrosion area can be obtained by selecting the input temperature as the temperature, the average temperature or the maximum temperature of each pixel point in the corrosion area, so that the comprehensiveness and diversity of the obtained data are ensured, the method is easy to realize in the aspect of procedure, has the advantages of high calculation speed and intuitive result, and can provide a relatively accurate reference for corrosion detection personnel;
3. meanwhile, the area of the corrosion area can be calculated through the outline of the corrosion area, and more information about the corrosion condition of the metal component can be obtained.
Drawings
FIG. 1 is a flow chart of a method for infrared detection of corrosion of a metal component according to the present invention;
FIG. 2 is a diagram of a thermal conductivity model of an unetched region in constructing a temperature corrosion depth conversion model;
FIG. 3 is a diagram of a thermal conductivity model of a corrosion region when constructing a temperature corrosion depth conversion model;
FIG. 4 is an infrared detection system for corrosion of metal components used to obtain infrared images in accordance with a preferred embodiment of the present invention.
The same reference numbers will be used throughout the drawings to refer to the same or like elements or structures, wherein:
1-computer analysis equipment, 2-infrared thermal imaging equipment, 3-hot air equipment and 4-metal components.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in FIG. 1, the invention provides an infrared detection method for corrosion condition of a metal component, which comprises the following steps:
s1, acquiring an infrared image of the metal component, performing filtering and denoising by adopting an edge preserving filtering method, and then setting a threshold value to acquire a binary image of the infrared image;
s2, marking an erosion area and an erosion-free area according to connectivity of each pixel and adjacent pixels in the binary image, drawing the outline of the erosion area, and calculating the area of the erosion area through pixel points in the outline;
s3, extracting a mask of the erosion area in the binary image according to the outline of the erosion area, and calculating the temperature of the erosion area in the infrared image by using the mask;
s4, inputting the temperature of the corrosion area to obtain the corrosion depth of the metal component by using an implicit function of the corrosion depth Dep in the temperature corrosion depth conversion model shown in the formula (1);
in which Dep is the depth of etching, AkCoefficient k for Fourier expansion, βkIs the k characteristic value, BkCoefficient k of Fourier expansionkIs the k characteristic value which is a function of the depth of etching Dep, tau is the heating time, a1Thermal diffusivity for corrosion layers, a2Thermal diffusivity of an unetched layer, thickness of a metal member, t0Is the initial temperature of the metal member, tfTo heat the temperature, λ1To the thermal conductivity of the metal of the corrosion layer, lambda2The thermal conductivity of the metal of the non-corroded layer, h is the surface heat transfer coefficient of the metal member, TbTemperature of non-corroded area, T1Is the temperature of the corrosion region.
Further, in step S1, an Otsu binarization algorithm or a maximum normalized temperature difference method is used to obtain a binarization image of the infrared image, wherein when the Otsu binarization algorithm is used, firstly, a gaussian kernel is used for denoising according to the size of the infrared image, and then the Otsu binarization algorithm is used;
the specific process of the maximum normalized temperature difference method is as follows:
setting delta T ' as a threshold value for judging whether the corrosion is caused or not, marking the target area as a corrosion area if the delta T ' is larger than 0, and marking the target area as a non-corrosion area if the delta T ' is equal to 0;
where T is the average temperature of the target region, TMIs the highest temperature of the target area, TmIs the lowest temperature, T, of the target areabIs the average temperature, T, of the non-corroded areabMMaximum temperature of non-corroded area, TbmThe lowest temperature of the non-corroded area.
Further, in step S3, the temperature of the corrosion region is obtained according to the pixel value of the corrosion region, the average pixel value of the corrosion region is subtracted from the average pixel value of the non-corrosion region, the corrosion is more serious the larger the obtained color difference is, and the temperature of the corrosion region includes the temperature of each pixel point in the corrosion region, the average temperature of the corrosion region, and the maximum temperature of the corrosion region.
Further, in step S4, the method for constructing the temperature corrosion depth conversion model includes the following sub-steps:
s41 is to simplify the calculation, only consider the heat transfer of the metal component along the thickness longitudinal direction, neglect its horizontal heat transfer, so can simplify to the one-dimensional unsteady state heat conduction problem, FIG. 2 is the heat conduction model diagram of the non-corrosion area, construct the model of the non-corrosion area;
differential equation of heat conduction:
initial conditions: t (x,0) ═ t0(4)
Boundary conditions:
wherein x is the value of the metal member in the thickness direction, τ is the heating time, t (x, τ) is the temperature at the time of τ at x, a2Thermal diffusivity, t, of an unetched layer0Is the ambient temperature, i.e., the initial temperature of the metal member, h is the surface heat transfer coefficient of the metal member, tfTo heat the temperature, λ2The metal thermal conductivity of the non-corrosion layer is the thickness of the metal component, t (0, tau) is the temperature at the surface of the metal component at the time tau, and t (tau) is the temperature at the thickness of the metal component at the time tau;
the partial differential equation can be solved by a separation variable method or a green function method as follows:
in the formula, AkCoefficient k for Fourier expansion, βkIs the k characteristic value;
from characteristic equations
Solve the eigenvalue βkAnd 0 is<β1<β2<β3<…<βkAccording to the initial condition t (x,0) ═ t0The coefficient A of Fourier expansion is obtained by Fourier expansionkBecause the infinite series in the equation solution gradually attenuates, the first terms can be taken as appropriate to be approximated according to the requirement of detection precision;
since the thermal imaging camera collects the temperature information of the surface of the metal component, namely T (0, tau) is a known quantity and is recorded as TbThe temperature T of the non-corroded area is expressed by the partial differential equation shown in the formula (8)bComprises the following steps:
s42 FIG. 3 is a heat conduction model diagram of a corrosion region, and a model of the corrosion region with a corrosion depth Dep is constructed;
differential equation of heat conduction:
initial conditions: t is ti(x,0)＝t0(i＝1,2) (10)
Boundary conditions:
(t1-t2)|x＝Dep＝0 (13)
wherein, when i is 1, it represents an etched layer, when i is 2, it represents an unetched layer, and aiDenotes the thermal diffusivity, λ, of the i-th layeriDenotes the thermal conductivity of the metal of the i-th layer, ti(x, τ) is the temperature at time τ at the ith layer x, ti(x,0) is the initial temperature at the ith layer x, t1(0, τ) is the temperature at time τ on the surface of the corrosion layer, λ1To the thermal conductivity of the metal of the corrosion layer, lambda2Thermal conductivity of the metal as an unetched layer, t2(. tau.) is the temperature at time tau at the thickness of the metal component, t1Temperature to etch the layer, t2The temperature of the non-corroded layer;
the differential equation can be solved here by the extended separation variational method as follows:
in the formula, t1(x, τ) is the temperature at time τ at the etch layer x, t2(x, τ) is the temperature at time τ at the non-etched layer x, BkCoefficient k for Fourier expansion, CkCoefficient k of Fourier expansionkIs the k characteristic value, mukIs the k characteristic value;
the characteristic value gamma is solved by a characteristic equation set shown in a formula (16)kAnd mukAnd 0 is<γ1<γ2<γ3<…<γk、0<μ1<μ2<μ3<…<μkWherein γ iskAnd mukAre all functions related to the depth of erosion Dep;
according to the initial condition ti(x,0)＝t0(i 1,2) by fourier expansion(Fourier series) to obtain each coefficient BkAnd CkBecause the infinite series in the equation solution gradually attenuates, the first terms can be taken as appropriate to be approximated according to the requirement of detection precision;
the thermal imaging camera collects the temperature information of the surface of the metal component, i.e. t1(0, τ) is a known quantity, denoted T1The temperature T of the corrosion zone can be determined by the solution of the above partial differential equation1Expressed as:
s43 formula (8) and formula (17) are implicit functions with respect to the etch depth Dep, and the implicit function to obtain the etch depth Dep is:
the etch depth Dep may be abbreviated as Dep ═ f3(ai,t0,tf,λi,τ,Tb,T1) According to the infrared detection condition, the temperature T of the corrosion area is measured1As input, the etching depth Dep can be obtained;
when temperature T of the corrosion area1Obtaining the corrosion depth of each pixel point when the temperature of each pixel point is the temperature T of the corrosion area1The average corrosion depth of the corrosion area can be obtained when the average temperature of the corrosion area is T1The maximum etch depth of the etch region is achieved at the maximum temperature of the etch region.
An infrared detection system for the corrosion condition of the metal member as shown in fig. 4 is adopted to obtain an infrared image of the metal member, and the system comprises a computer analysis device 1, an infrared thermal imaging device 2 and a hot air device 3, wherein the infrared thermal imaging device 2 is connected with the computer analysis device 1 through a data line, a lens of the infrared thermal imaging device 2 is positioned in front of a detection coil, the hot air device 3 thermally loads the metal member 4 when in operation, the corrosion can change the thermal diffusivity and the heat storage performance of the metal, so the temperature of a corrosion area is higher than that of a non-corrosion area, a temperature difference is formed, the infrared imaging device 2 is used for carrying out real-time infrared image acquisition on the surface temperature field of the metal member 4, and the acquired infrared image is transmitted to the computer analysis device 1.
In a preferred embodiment of the present invention, the infrared thermal imaging device 2 is a model F L IR a320, the metal member is a car B-pillar, the infrared thermal imaging device 2 is first fixed, and its lens is aligned with the detection position of the metal member 4, the lens is about 0.5m away from the metal member 4, the focal length is adjusted, the initial temperature is set to room temperature, and the infrared thermal imaging device 2 is connected to the computer 1 through a data line;
the hot air device 3 is used for carrying out hot loading on the metal component 4 to 50-100 ℃, so that a temperature difference is formed between a corroded area and a non-corroded area of the metal component, the infrared thermal imaging equipment 2 is used for carrying out real-time infrared image acquisition on a surface temperature field of the metal component, and the acquired information is transmitted to a computer analysis platform;
finally, whether a region with abnormal temperature exists in the infrared image and the area and color difference of the region with abnormal temperature are analyzed by using the metal member corrosion condition detection method provided by the invention, so that the corrosion area and the corrosion depth are determined.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. An infrared detection method for corrosion condition of a metal component is characterized by comprising the following steps:
s1, acquiring an infrared image of the metal component, carrying out filtering and denoising treatment, and then setting a threshold value to acquire a binary image of the infrared image;
s2, marking a corroded area and an unetched area according to the connectivity of each pixel and the adjacent pixel in the binary image, and drawing the outline of the corroded area;
s3, extracting a mask of the corroded area in the binary image according to the outline of the corroded area, and calculating the temperature of the corroded area in the infrared image by using the mask;
s4, inputting the temperature of the corrosion area into a temperature corrosion depth conversion model to obtain the corrosion depth of the metal component, wherein the process of constructing the temperature corrosion depth conversion model comprises the following substeps:
s41, modeling the non-eroded area:
differential equation of heat conduction:
initial conditions: t (x,0) ═ t0(4)
Boundary conditions:
wherein x is the value of the metal member in the thickness direction, τ is the heating time, t (x, τ) is the temperature at the time of τ at x, a2Thermal diffusivity, t, of an unetched layer0Is the ambient temperature, i.e., the initial temperature of the metal member, h is the surface heat transfer coefficient of the metal member, tfTo heat the temperature, λ2The metal thermal conductivity of the non-corrosion layer is the thickness of the metal component, t (0, tau) is the temperature at the surface of the metal component at the time tau, and t (tau) is the temperature at the thickness of the metal component at the time tau;
the partial differential equation can be solved by a separation variable method or a green function method as follows:
in the formula, AkCoefficient k for Fourier expansion, βkIs the k characteristic value;
from characteristic equations
The characteristic value β can be solvedkAnd 0 is<β1<β2<β3<…<βk(ii) a Obtaining the coefficient A by Fourier expansion according to the initial conditionk；
T (0, τ) is the surface temperature of the non-etched region and is denoted as TbThe temperature T of the non-corroded area can be adjustedbExpressed as:
s42, constructing a model of the corrosion region with the corrosion depth Dep:
differential equation of heat conduction:
initial conditions: t is ti(x，0)＝t0(i＝1,2) (10)
Boundary conditions:
(t1-t2)|x＝Dep＝0 (13)
wherein x is a value of the metal member in the thickness direction, τ is a heating time, i is 1 to indicate an etched layer, i is 2 to indicate an unetched layer, and a isiDenotes the thermal diffusivity, λ, of the i-th layeriDenotes the thermal conductivity of the metal of the i-th layer, ti(x, τ) is the temperature at time τ at the ith layer x, ti(x,0) is the initial temperature at the ith layer x, tfTo heat the temperature, t1(0, τ) is the surface of the etch layerTemperature at time τ, t2(. tau) is the temperature at time tau of the thickness of the metal component of the non-corroded layer, t0Is the ambient temperature, i.e., the initial temperature of the metal member, h is the surface heat transfer coefficient of the metal member, t1Temperature to etch the layer, t2The temperature of the non-corrosion layer is shown, and Dep is the depth of the metal corrosion layer;
the differential equation can be solved by an extended separation variable method as follows:
in the formula, t1(x, τ) is the temperature at time τ at the etch layer x, t2(x, τ) is the temperature at time τ at the non-etched layer x, BkCoefficient k for Fourier expansion, CkCoefficient k of Fourier expansionkIs the k characteristic value, mukIs the k characteristic value;
from the system of characteristic equations (16), the characteristic value gamma can be solvedkAnd mukAnd 0 is<γ1<γ2<γ3<…<γk、0<μ1<μ2<μ3<…<μkWherein γ iskAnd mukAre all functions relating to the depth of erosion Dep:
according to the initial condition, using Fourier expansion (Fourier series) to obtain each coefficient BkAnd Ck；
t1(0, τ) surface temperature of the etched region, noted T1The temperature T of the corrosion area can be adjusted1Expressed as:
s43 obtains an implicit function with respect to the etch depth Dep according to equation (8) and equation (17) as:
the etch depth Dep may be abbreviated as Dep ═ f3(ai，t0，tf,λi,τ，Tb，T1) Inputting the temperature T of the corrosion region according to the infrared detection condition1And temperature T of non-corroded areabThen the etching depth Dep can be calculated.
2. The method of claim 1, wherein in step S1, the filtering and de-noising process is performed by using an edge preserving filter.
3. The infrared detection method of the corrosion condition of the metal member as claimed in claim 1, wherein in step S1, a binarized image of the infrared image is obtained using Otsu binarization algorithm or maximum normalized temperature difference method.
4. The method of any one of claims 1 to 3, wherein in step S3, the temperature of the corrosion region includes the temperature of each pixel in the corrosion region, the average temperature of the corrosion region, or the maximum temperature of the corrosion region.
5. The method of claim 1, wherein step S2 further comprises calculating the area of the corroded area according to the outline of the corroded area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910316762.5A CN110057745B (en) | 2019-04-19 | 2019-04-19 | Infrared detection method for corrosion condition of metal component |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910316762.5A CN110057745B (en) | 2019-04-19 | 2019-04-19 | Infrared detection method for corrosion condition of metal component |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110057745A CN110057745A (en) | 2019-07-26 |
| CN110057745B true CN110057745B (en) | 2020-07-10 |
Family
ID=67319713
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910316762.5A Active CN110057745B (en) | 2019-04-19 | 2019-04-19 | Infrared detection method for corrosion condition of metal component |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110057745B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112233109B (en) * | 2020-11-05 | 2022-10-14 | 北京理工大学 | Visible light interference resistant metal feeding visual sorting method |
| CN112251752B (en) * | 2020-12-23 | 2021-04-16 | 成都裕鸢航空智能制造股份有限公司 | Method for removing broken tap or broken screw and filling device |
| CN117309668A (en) * | 2023-09-21 | 2023-12-29 | 东南大学 | Portable automatic detection equipment and detection method for steel wire corrosion |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103111427B (en) * | 2013-01-10 | 2014-08-27 | 上海交通大学 | Fruit grader based on image processing |
| CN109448009A (en) * | 2018-11-21 | 2019-03-08 | 国网江苏省电力有限公司扬州供电分公司 | Infrared Image Processing Method and device for transmission line faultlocating |
-
2019
- 2019-04-19 CN CN201910316762.5A patent/CN110057745B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN110057745A (en) | 2019-07-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110057745B (en) | Infrared detection method for corrosion condition of metal component | |
| CN108198181B (en) | Infrared thermal image processing method based on region segmentation and image fusion | |
| EP2350627B1 (en) | Method for detecting defect in material and system for the method | |
| Grys | New thermal contrast definition for defect characterization by active thermography | |
| CN110186570B (en) | Additive manufacturing laser 3D printing temperature gradient detection method | |
| CN112461892A (en) | Infrared thermal image analysis method for nondestructive detection of composite material defects | |
| CN112837294B (en) | Thermal imaging defect detection method based on convolution self-encoder image amplification | |
| CN112241970B (en) | Schlieren imaging-based gas-liquid two-phase gas-liquid flow field velocity measuring method and system | |
| JP2019066465A (en) | Background radiance estimation and gas concentration-length quantification method for optical gas imaging camera | |
| Grys et al. | Size determination of subsurface defect by active thermography–Simulation research | |
| Atwya et al. | Transient thermography for flaw detection in friction stir welding: A machine learning approach | |
| CN112304478B (en) | Residual stress testing method based on creep profile method | |
| Shafi et al. | Internal defects detection and classification in hollow cylindrical surfaces using single shot detection and MobileNet | |
| CN113705564B (en) | Pointer type instrument identification reading method | |
| Hao et al. | Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks | |
| CN110880170B (en) | Depth prediction method for composite material defects | |
| CN109816651B (en) | Thermal image defect feature extraction method based on change rate and temperature difference | |
| Pan et al. | On-line bleeds detection in continuous casting processes using engineering-driven rule-based algorithm | |
| CN105866168A (en) | Identification method and apparatus for lower matrix material of coating | |
| Christian et al. | Real-time quantification of damage in structural materials during mechanical testing | |
| Vozmilov et al. | Development of an algorithm for the program to recognize defects on the surface of hot-rolled metal | |
| Wronkowicz et al. | Enhancement of damage identification in composite structures with self-heating based vibrothermography | |
| CN113884538A (en) | Infrared thermal image detection method for micro defects in large wind turbine blade | |
| CN110111277B (en) | Planar thermal image repairing method and device | |
| CN109886930B (en) | Thermal image defect feature extraction method based on change rate and temperature difference |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |