CN109658389A - A kind of corrosion image binary processing method - Google Patents
A kind of corrosion image binary processing method Download PDFInfo
- Publication number
- CN109658389A CN109658389A CN201811449309.3A CN201811449309A CN109658389A CN 109658389 A CN109658389 A CN 109658389A CN 201811449309 A CN201811449309 A CN 201811449309A CN 109658389 A CN109658389 A CN 109658389A
- Authority
- CN
- China
- Prior art keywords
- image
- corrosion
- testpieces
- processing method
- erosion profile
- 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
- 238000005260 corrosion Methods 0.000 title claims abstract description 60
- 230000007797 corrosion Effects 0.000 title claims abstract description 60
- 238000003672 processing method Methods 0.000 title claims abstract description 22
- 230000003628 erosive effect Effects 0.000 claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 11
- 238000010865 video microscopy Methods 0.000 claims abstract description 9
- CSCPPACGZOOCGX-UHFFFAOYSA-N Acetone Chemical compound CC(C)=O CSCPPACGZOOCGX-UHFFFAOYSA-N 0.000 claims description 6
- 229910000838 Al alloy Inorganic materials 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 229910052751 metal Inorganic materials 0.000 claims description 3
- 239000002184 metal Substances 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 abstract description 2
- 239000007769 metal material Substances 0.000 abstract description 2
- 238000011156 evaluation Methods 0.000 description 2
- 241001270131 Agaricus moelleri Species 0.000 description 1
- 229910052782 aluminium Inorganic materials 0.000 description 1
- 239000004411 aluminium Substances 0.000 description 1
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
-
- 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
- G06T2207/30136—Metal
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Quality & Reliability (AREA)
- Geometry (AREA)
- Image Processing (AREA)
Abstract
The application belongs to metallic material corrosion field, in particular to a kind of corrosion image binary processing method.Include: step 1: testpieces is handled；Step 2: being observed by videomicroscopy and shoots the etch pit microscopic appearance figure of testpieces；Step 3: reading the etch pit microscopic appearance figure of testpieces, is pre-processed using binarization method to etch pit microscopic appearance figure, generates basic erosion profile grayscale image；Step 4: reading basic erosion profile grayscale image, is handled using improved Otsu algorithm basic erosion profile grayscale image, generates binary image；Wherein, the improved Otsu algorithm is that the marginal information of basic erosion profile grayscale image and the Otsu algorithm of grayscale information is added.The application further carries out binary conversion treatment to image using improved Otsu algorithm, can preferably restore the erosion profile in corrosion image, more acurrate to the processing of image, reduces noise, and eliminate many disturbing factors in former erosion profile grayscale image.
Description
Technical field
The application belongs to metallic material corrosion field, in particular to a kind of corrosion image binary processing method.
Background technique
It is different from real-time corrosion detection emphasis, commenting for extent of corrosion more is carried out using corrosion image in laboratory
Valence, to obtain the evaluation parameters such as rate of corrosion.The research of corrosion image processing technique to using corrosion image carry out corrosion diagnosis with
The accuracy of evaluation is closely bound up.The corrosion information of one untreated original corrosion image transmitting may be unintelligible, no
Explicitly, various parameters obtained also can there are large errors with actual corrosion condition directly after analysis.
Image binaryzation is a basic fundamental in image procossing, and many image processing techniques is pretreated
Journey.But the disadvantages of there are anti-noise abilities in existing binarization method poor, edge roughness, artifact phenomenon.
Thus, it is desirable to have a kind of technical solution overcomes or at least mitigates at least one drawbacks described above of the prior art.
Summary of the invention
The purpose of the application there is provided a kind of corrosion image binary processing method, with solve it is of the existing technology extremely
A few problem.
The technical solution of the application is:
A kind of corrosion image binary processing method, comprising:
Step 1: testpieces is handled；
Step 2: being observed by videomicroscopy and shoots the etch pit microscopic appearance figure of testpieces；
Step 3: the etch pit microscopic appearance figure of testpieces is read, using binarization method to etch pit microscopic appearance figure
It is pre-processed, generates basic erosion profile grayscale image；
Step 4: reading basic erosion profile grayscale image, using improved Otsu algorithm to basic erosion profile grayscale image
It is handled, generates binary image；
Wherein, improved Otsu algorithm described in step 4 is the marginal information and ash that basic erosion profile grayscale image is added
Spend the Otsu algorithm of information.
Optionally, carrying out processing to testpieces in step 1 includes:
Corrosion product on S101, removal testpieces；
S102, using 3% metal cleaner to testpieces oil removing；
S103, testpieces surface is cleaned comprehensively using acetone；
S104, testpieces is dried；
S105, it is polished using waterproof abrasive paper testpieces.
Optionally, the waterproof abrasive paper includes 500# waterproof abrasive paper and 1000# waterproof abrasive paper.
Optionally, the videomicroscopy is UNION DZ3 continuous vari-focus microscope.
Optionally, the videomicroscopy is SEM scanning electron microscope.
Optionally, in the improved Otsu algorithm, the threshold values of basic erosion profile grayscale image image passes through adjacent edges
Seed point is determined in the filling in high threshold image and the repairing in Low threshold image.
Optionally, the testpieces is corrosion of aluminium alloy part.
Optionally, the color point in the binary image is divided into hole pitting and matrix.
Optionally, further includes:
Step 5: carrying out feature extraction to corrosion area and non-corrosion area according to the value of the pixel of binary image,
Calculate rate of corrosion P.
Optionally, the rate of corrosion P is the area ratio of corrosion area and whole region.
At least there are following advantageous effects in invention:
The corrosion image binary processing method of the application carries out at binaryzation image using improved Otsu algorithm
Reason, it is more acurrate to the processing of image, noise is reduced, many disturbing factors can be rejected.
Detailed description of the invention
Fig. 1 is the flow chart of the application corrosion image binary processing method；
Fig. 2 is the improved Otsu algorithm flow chart of the application.
Specific embodiment
To keep the purposes, technical schemes and advantages of the application implementation clearer, below in conjunction in the embodiment of the present application
Attached drawing, technical solutions in the embodiments of the present application is further described in more detail.In the accompanying drawings, identical from beginning to end or class
As label indicate same or similar element or element with the same or similar functions.Described embodiment is the application
A part of the embodiment, instead of all the embodiments.The embodiments described below with reference to the accompanying drawings are exemplary, it is intended to use
In explanation the application, and it should not be understood as the limitation to the application.Based on the embodiment in the application, ordinary skill people
Member's every other embodiment obtained without creative efforts, shall fall in the protection scope of this application.Under
Face is described in detail embodiments herein in conjunction with attached drawing.
In the description of the present application, it is to be understood that term " center ", " longitudinal direction ", " transverse direction ", "front", "rear",
The orientation or positional relationship of the instructions such as "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outside" is based on attached drawing institute
The orientation or positional relationship shown is merely for convenience of description the application and simplifies description, rather than the dress of indication or suggestion meaning
It sets or element must have a particular orientation, be constructed and operated in a specific orientation, therefore should not be understood as protecting the application
The limitation of range.
1 to Fig. 2 the application is described in further details with reference to the accompanying drawing.
This application provides a kind of corrosion image binary processing method, method includes:
Step 1: testpieces is handled；
Step 2: being observed by videomicroscopy and shoots the etch pit microscopic appearance figure of testpieces；
Step 3: the etch pit microscopic appearance figure of testpieces is read, using binarization method to etch pit microscopic appearance figure
It is pre-processed, generates basic erosion profile grayscale image；
Step 4: reading basic erosion profile grayscale image, using improved Otsu algorithm to basic erosion profile grayscale image
It is handled, generates binary image；
Wherein, improved Otsu algorithm is the marginal information and gray scale letter that basic erosion profile grayscale image is added in step 4
The Otsu algorithm of breath.
In the embodiment of the application, testpieces is corrosion of aluminium alloy part, in step 1 to testpieces at
Reason includes:
Corrosion product on S101, removal testpieces；
S102, using 3% metal cleaner to testpieces oil removing；
S103, testpieces surface is cleaned using acetone comprehensively, removes spot；
S104, testpieces is dried；
S105, it is polished using waterproof abrasive paper testpieces.
In the present embodiment, polished using 500# waterproof abrasive paper and 1000# waterproof abrasive paper testpieces.
Videomicroscopy in step 2 can be UNION DZ3 continuous vari-focus microscope or SEM scanning electron microscope etc..
In the corrosion image binary processing method of the application, the etch pit of testpieces is read by MATLAB software first
Microscopic appearance figure pre-processes etch pit microscopic appearance figure using traditional binarization method, generates basic erosion profile
Grayscale image；Then basic erosion profile grayscale image is read by MATLAB software again, using improved Otsu algorithm to substantially rotten
Erosion pattern grayscale image is handled, and final binary image is generated.In the present embodiment, by the marginal information and ash of corrosion interface
Degree information is added in classical Otsu algorithm, and in improved Otsu algorithm, the threshold values of basic erosion profile grayscale image image is logical
Repairing of the adjacent edges seed point in the filling and Low threshold binary image in high threshold binary image is crossed to determine.
Classical Otsu algorithm is improved, it is more acurrate to the processing of image, noise is reduced, it is also more acurrate to the reduction of image border.
Further, using the corrosion image binary processing method of the application, after corrosion image binary conversion treatment, aluminium is closed
Etch pit can be distinguished clearly with the matrix of aluminium alloy in gold, and the number of etch pit is also easily identified.Binaryzation
There was only black and pure white two kinds of gray scales in image, the color point enabled aluminum alloy in surface holes corrosion figure picture is divided into hole pitting (foreground point
Grey scale pixel value is l) and matrix (background dot grey scale pixel value be 0).
After to corrosion image binaryzation, according to the value of the pixel of binary image to corrosion area and non-corrosion area
Feature extraction is carried out, rate of corrosion P is calculated, rate of corrosion P is the area ratio of corrosion area and whole region.
The corrosion image binary processing method of the application further carries out two-value to image using improved Otsu algorithm
Change processing, can preferably restore the erosion profile in corrosion image, more acurrate to the processing of image, reduce noise, and
Eliminate many disturbing factors in former erosion profile grayscale image.
The above, the only specific embodiment of the application, but the protection scope of the application is not limited thereto, it is any
Within the technical scope of the present application, any changes or substitutions that can be easily thought of by those familiar with the art, all answers
Cover within the scope of protection of this application.Therefore, the protection scope of the application should be with the scope of protection of the claims
It is quasi-.
Claims (10)
1. a kind of corrosion image binary processing method characterized by comprising
Step 1: testpieces is handled；
Step 2: being observed by videomicroscopy and shoots the etch pit microscopic appearance figure of testpieces；
Step 3: reading the etch pit microscopic appearance figure of testpieces, is carried out using binarization method to etch pit microscopic appearance figure
Pretreatment, generates basic erosion profile grayscale image；
Step 4: reading basic erosion profile grayscale image, is carried out using improved Otsu algorithm to basic erosion profile grayscale image
Processing generates binary image；
Wherein, improved Otsu algorithm described in step 4 is the marginal information and gray scale letter that basic erosion profile grayscale image is added
The Otsu algorithm of breath.
2. corrosion image binary processing method according to claim 1, which is characterized in that in step 1 to testpieces into
Row is handled
Corrosion product on S101, removal testpieces；
S102, using 3% metal cleaner to testpieces oil removing；
S103, testpieces surface is cleaned comprehensively using acetone；
S104, testpieces is dried；
S105, it is polished using waterproof abrasive paper testpieces.
3. corrosion image binary processing method according to claim 2, which is characterized in that the waterproof abrasive paper includes 500#
Waterproof abrasive paper and 1000# waterproof abrasive paper.
4. corrosion image binary processing method according to claim 1, which is characterized in that the videomicroscopy is
UNION DZ3 continuous vari-focus microscope.
5. corrosion image binary processing method according to claim 1, which is characterized in that the videomicroscopy is
SEM scanning electron microscope.
6. corrosion image binary processing method according to claim 1, which is characterized in that the improved Otsu algorithm
In, the threshold values of basic erosion profile grayscale image image passes through filling and low threshold of the adjacent edges seed point in high threshold image
Repairing in value image determines.
7. corrosion image binary processing method according to claim 1, which is characterized in that the testpieces is aluminium alloy
Corrode part.
8. corrosion image binary processing method according to claim 7, which is characterized in that in the binary image
Color point is divided into hole pitting and matrix.
9. corrosion image binary processing method according to claim 1, which is characterized in that further include:
Step 5: feature extraction is carried out to corrosion area and non-corrosion area according to the value of the pixel of binary image, is calculated
Rate of corrosion P.
10. corrosion image binary processing method according to claim 9, which is characterized in that the rate of corrosion P is corrosion
The area ratio in region and whole region.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811449309.3A CN109658389A (en) | 2018-11-30 | 2018-11-30 | A kind of corrosion image binary processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811449309.3A CN109658389A (en) | 2018-11-30 | 2018-11-30 | A kind of corrosion image binary processing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109658389A true CN109658389A (en) | 2019-04-19 |
Family
ID=66111120
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811449309.3A Pending CN109658389A (en) | 2018-11-30 | 2018-11-30 | A kind of corrosion image binary processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109658389A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111028229A (en) * | 2019-12-19 | 2020-04-17 | 中国特种飞行器研究所 | Metal or coating corrosion detection method based on image processing technology |
| CN115345879A (en) * | 2022-10-18 | 2022-11-15 | 济宁康盛彩虹生物科技有限公司 | Method for analyzing corrosion degree of inner wall of autoclave and predicting service life of autoclave based on image |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105092461A (en) * | 2015-09-14 | 2015-11-25 | 北京科技大学 | Metal corrosion high-throughput characterization experimental apparatus based on image |
| CN205015259U (en) * | 2015-09-14 | 2016-02-03 | 北京科技大学 | Corrosion of metals high flux sign experimental apparatus based on image |
-
2018
- 2018-11-30 CN CN201811449309.3A patent/CN109658389A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105092461A (en) * | 2015-09-14 | 2015-11-25 | 北京科技大学 | Metal corrosion high-throughput characterization experimental apparatus based on image |
| CN205015259U (en) * | 2015-09-14 | 2016-02-03 | 北京科技大学 | Corrosion of metals high flux sign experimental apparatus based on image |
Non-Patent Citations (5)
| Title |
|---|
| 陈定海 等: "基于二值图像的铝合金加速腐蚀损伤演化规律研究" * |
| 陈定海等: "基于二值图像的铝合金加速腐蚀损伤演化规律研究", 《腐蚀科学与防护技术》 * |
| 陈强 等: "结合Canny 算子的图像二值化" * |
| 马国锐 等: "一种高分辨率遥感影像码头轮廓提取方法" * |
| 马国锐等: "一种高分辨率遥感影像码头轮廓提取方法", 《遥感信息》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111028229A (en) * | 2019-12-19 | 2020-04-17 | 中国特种飞行器研究所 | Metal or coating corrosion detection method based on image processing technology |
| CN115345879A (en) * | 2022-10-18 | 2022-11-15 | 济宁康盛彩虹生物科技有限公司 | Method for analyzing corrosion degree of inner wall of autoclave and predicting service life of autoclave based on image |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110717888B (en) | Automatic identification method for intravascular Optical Coherence Tomography (OCT) vessel wall inner contour | |
| CN109241973B (en) | Full-automatic soft segmentation method for characters under texture background | |
| KR20110002858A (en) | Filtering method and apparatus for anti-aliasing | |
| CN110956612A (en) | Cell rapid counting method, computer equipment and readable storage medium | |
| CN109658389A (en) | A kind of corrosion image binary processing method | |
| CN101908205B (en) | Magic square coding-based median filter method | |
| CN110533047A (en) | A kind of denoising and binarization method for ancient books picture | |
| CN111192273A (en) | Digital shot blasting coverage rate measuring method based on computer vision technology | |
| CN113592782B (en) | Method and system for extracting X-ray image defects of composite material carbon fiber core rod | |
| CN109781737A (en) | A kind of detection method and its detection system of hose surface defect | |
| Sehad et al. | Gabor filters for degraded document image binarization | |
| CN113362280B (en) | Dynamic target tracking method based on medical radiography | |
| CN108205678A (en) | A kind of nameplate Text region processing method containing speck interference | |
| Choi et al. | Deep learning-based engraving segmentation of 3-d inscriptions extracted from the rough surface of ancient stelae | |
| Milewski et al. | Binarization and cleanup of handwritten text from carbon copy medical form images | |
| Tie-Rui et al. | The research of X-ray bone fracture image enhancement algorithms | |
| Magnier et al. | A quantitative error measure for the evaluation of roof edge detectors | |
| Zaim | Automatic segmentation of the prostate from ultrasound data using feature-based self organizing map | |
| CN113940704A (en) | Thyroid-based muscle and fascia detection device | |
| Stach et al. | 3D image multifractal analysis and pore detection on a stereometric measurement file of a ceramic coating | |
| CN109753888A (en) | A method of solar filament is chiral in astronomical image for identification | |
| Brancati et al. | Image segmentation via iterative histogram thresholding and morphological features analysis | |
| CN109919853B (en) | PIV image preprocessing method based on particle feature retrieval | |
| Wang et al. | Improved cell segmentation with adaptive bi-Gaussian mixture models for image contrast enhancement pre-processing | |
| Van et al. | Stroke width-based contrast feature for document image binarization |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190419 |
|
| RJ01 | Rejection of invention patent application after publication |