CN111815575A - Bearing steel ball part detection method based on machine vision - Google Patents
Bearing steel ball part detection method based on machine vision Download PDFInfo
- Publication number
- CN111815575A CN111815575A CN202010564947.0A CN202010564947A CN111815575A CN 111815575 A CN111815575 A CN 111815575A CN 202010564947 A CN202010564947 A CN 202010564947A CN 111815575 A CN111815575 A CN 111815575A
- Authority
- CN
- China
- Prior art keywords
- image
- edge
- steel ball
- detection
- detecting
- 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.)
- Granted
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 48
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 33
- 239000010959 steel Substances 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 claims abstract description 30
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 238000012216 screening Methods 0.000 claims abstract description 10
- 238000003708 edge detection Methods 0.000 claims description 14
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000003384 imaging method Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000011410 subtraction method Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 230000007547 defect Effects 0.000 description 4
- 238000009776 industrial production Methods 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 238000005286 illumination Methods 0.000 description 2
- 238000000465 moulding Methods 0.000 description 2
- 241000282412 Homo Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 238000003754 machining Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 210000001363 mesenteric artery superior Anatomy 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012634 optical imaging Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000007650 screen-printing Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
-
- 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
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- 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
- G06T7/10—Segmentation; Edge detection
- G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- 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)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
A bearing steel ball part detection method based on machine vision comprises steel ball circularity detection and part scratch detection and comprises the following steps: the method comprises the following steps of firstly, detecting the circularity of a steel ball, and the process is as follows: 1.1) preprocessing an input image; 1.2) acquiring the outline of the steel ball; 1.3) screening the obtained edges, and outputting the outline and circle center radius of the steel ball qualified by detection; step two, detecting scratches of the parts: 2.1) detecting the edge of the front surface of the part; 2.2) detecting the side edge of the part. The invention provides a bearing steel ball part detection method based on machine vision, which can overcome various interference factors and establish a high-efficiency and stable detection system.
Description
Technical Field
The invention relates to the field of part detection, in particular to a bearing steel ball part detection method based on machine vision.
Background
While promoting social progress, humans have given machines a lot of complex work with a great deal of repeatability, and machine vision systems are in this category. Generally, machine vision systems are industrial test monitoring systems. In some dangerous work places or situations where the identification by human eyes is difficult, machine vision systems are often used to improve the quality or automation of the production line. Machine vision technology is one direction in the detection field, and aims to replace human eyes with machines to complete tasks of detection, identification, classification and the like. The complete machine vision technology comprises the technologies of light source illumination, optical imaging, digital image processing, machine classification, industrial control and the like, and has high stability and accuracy. Machine vision is mainly used in semiconductor, electronic, and mechanical industries, such as integrated circuit manufacturing, electronic molding, screen printing, and component molding. In addition, machine vision is also widely used in product quality inspection systems, and plays a significant role in industrial production.
The existing part detection method is generally based on a machine vision technology. A set of circular part detection framework is developed by researchers based on a machine vision technology, the precision measurement of the circular part is realized through simple camera calibration and sub-pixels, and an algorithm model mainly comprises four parts of system correction, preprocessing, image binarization and circular detection. The precision machining technology of the aspheric surface curved surface optical part is analyzed and researched, a set of ultra-precision air compression spindle system is developed, and the detection precision is improved to the international standard. Scanning an image of a computer hard disk sheet part to be detected by a linear array industrial camera, providing a new calibration algorithm and a new contour vectorization algorithm according to the characteristics of image scanning, and combining calibration, binaryzation, edge detection, contour vectorization and the like to obtain the size parameter of the part to be detected. Some researchers detect the defects on the surface of the part, accurately extract the detection area of the target part by performing a dynamic extraction algorithm twice, and then generate detection parameters based on a statistical chart classification algorithm and edge information such as the width, thickness, dispersion degree, deflection degree and the like of the part. On a modern automatic assembly line, the detection system can confirm the size, the number, the defect and the like of parts at any time and any place, and the industrial production efficiency is greatly improved. However, the problem of the current machine vision is also obvious, and the problems of time lag, low precision, poor stability and the like of the system are all needed to be solved urgently.
Disclosure of Invention
In order to overcome the defects that the parts cannot be detected quickly and accurately in the prior art and to solve the technical problems, the invention provides a bearing steel ball part detection method based on machine vision, which can overcome various interference factors and establish a high-efficiency and stable detection system.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a bearing steel ball part detection method based on machine vision is characterized by comprising the following steps:
the method comprises the following steps of firstly, detecting the circularity of a steel ball, and the process is as follows:
1.1) image preprocessing: preprocessing an input image by using Gaussian filtering to remove high-frequency signals in the image;
1.2) extracting superior mesenteric artery mouth: the image is converted by methods such as gray level conversion, threshold segmentation and the like, so that the subsequent algorithm can more easily identify image information; through this step, the system will get several closed edge profiles;
1.3) screening edge profiles: in order to obtain a better detection edge, three screening conditions are adopted: contour pixel domain detection, ellipse fitting detection and circularity detection, wherein the contour pixel domain detection is to count the pixel area of each contour; because the circular outline of the steel ball can form an ellipse similar to a circle after imaging, ellipse fitting is carried out, and noise interference can be effectively eliminated; finally, after the edge profile information of the steel ball is obtained, the circularity of the ellipse is detected, and the purpose of the step is to detect whether the shape of the steel ball meets the production specification;
secondly, detecting scratches of the parts, wherein the process is as follows:
2.1) detecting scratches on the side surfaces of the parts: detecting the scratches on the side surfaces of the parts by using an edge detection method, firstly determining edge pixels in an image, and then connecting the pixels together to form a required area boundary;
2.2) detecting scratches on the front surface of the part: because too much edge information on the front side can cause serious influence on the classification result, a method for eliminating redundant edge information is adopted to remove high-frequency signals in the image.
Further, 2.1), the image edge represents the beginning of one region and another region in the image, and the set of pixels between adjacent regions in the image constitutes the edge of the image, so the edge of the image can be understood as the set of pixels with spatial abrupt change of the gray level of the image, and the image edge has two important concepts: the direction and gradient of the pixel change along the edge direction are smooth, and the pixel change perpendicular to the edge direction is severe, so according to the characteristic, the edge detection is usually performed by using a first derivative and a second derivative, so that the edge detection in an image can be determined by differentiating the gray value, and the derivative operation can be performed by a differential operator.
Further, the step of 2.2) is as follows:
2.2.1) preprocessing the image by using smooth filtering to remove high-frequency signals in the image;
2.2.2) detecting the image edge information by adopting an edge detection method and extracting a closed contour of the image;
2.2.3) carrying out ellipse fitting on the outline by using an ellipse fitting method, wherein the ellipse fitting is carried out because the target area to be extracted is a circular ring;
2.2.4) selecting a proper threshold value according to the fixed information of the image, screening the obtained elliptic contour to obtain the inner circle contour and the outer circle contour of the circular ring area, and finally obtaining the target area to be extracted by adopting an image subtraction method.
The invention has the following beneficial effects: based on machine vision, the problem that defective products are difficult to distinguish manually in the industrial production process is solved. And the method has high stability and portability and wide application prospect.
Drawings
FIG. 1 is a machine vision inspection system of the present invention;
FIG. 2 is a schematic view of the circularity detection of the present invention;
fig. 3 is a front surface inspection flow of the component of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 3, a bearing steel ball part detection method based on machine vision mainly solves steel ball circularity detection and part scratch detection, and comprises the following steps:
the first step, steel ball circularity detection, the flow of the detection method is shown in figure 2, and the process is as follows:
1.1) preprocessing an input image, which comprises the following specific steps:
firstly, preprocessing an input image by using Gaussian filtering to remove high-frequency signals in the image;
1.2) obtaining the steel ball profile, which comprises the following steps:
after the preprocessed information is obtained, the image is converted by methods such as gray level conversion, threshold segmentation and the like, so that the subsequent algorithm can more easily distinguish the image information. Through the step, the system can obtain a plurality of closed edge profiles, wherein only one closed edge profile is the edge profile of the steel ball, and the other closed edge profiles are noise interference generated by illumination;
1.3) screening the obtained edge profile, and outputting the profile and the circle center radius of the steel ball qualified by detection, wherein the steps are as follows:
in order to obtain a better detection edge, the invention adopts three screening conditions: contour pixel domain detection, ellipse fitting detection and circularity detection. Contour pixel domain detection is the statistics of the pixel area of each contour. Because the circular outline of the steel ball can form an ellipse similar to a circle after imaging, the ellipse fitting is carried out, and noise interference can be effectively eliminated. And finally, after the edge profile information of the steel ball is obtained, performing circularity detection on the ellipse, wherein the circularity of the profile is changed within a certain error range after the system is imaged in order to detect whether the appearance of the steel ball meets the production specification, and the steel ball beyond the range is likely to be a part which has defects and needs to be removed. The pixel threshold herein is: 1600. 2000, threshold of circularity 0.980;
circularity is the complexity used to delineate the boundaries of an image object, the value of which is minimal when the object is a perfect circle. The most common circularity is the ratio of the square of the circumference to the area, which is larger the more complex the shape, and the circularity index takes a minimum value of 4 pi when it is perfectly circular. Taking C as a circularity index, P as a perimeter and A as an area, the calculation formula is as follows:
the above calculation method is relatively rough, and the invention adopts boundary energy to measure. Taking p as the distance from any point on the boundary to some starting point. At any point, the boundary has an instantaneous radius of curvature r (p), which is the radius of the circle tangent to the boundary from the geometric relationship. The curvature function at point p is as follows:
the function K (p) is a periodic function with a period p. The average energy per unit boundary length is calculated as follows:
the radius of the circle is taken as R. The curvature can be calculated from the chain code, so the boundary energy can be easily obtained. For a boundary of fixed area, the minimum boundary energy E of a circle0Comprises the following steps:
wherein p is the distance from any point on the boundary to a certain starting point, and R is the radius of the circle;
secondly, detecting scratches of the parts, wherein the process is as follows:
2.1) detecting the scratches on the side surfaces of the parts, which comprises the following basic steps:
the invention uses an edge detection method to detect the side scratch of the part, firstly determines edge pixels in the image, and then connects the pixels together to form the required area boundary. The image edge represents the beginning of one region and another region in the image, and the set of pixels between adjacent regions in the image constitutes the edge of the image. Therefore, the edge of the image can be understood as a collection of pixels in which the gray level of the image has a spatial abrupt change. There are two important concepts of image edges: direction and gradient. The pixel variation along the edge direction is relatively smooth, while the pixel variation perpendicular to the edge direction is relatively sharp. Therefore, according to this feature, edge detection is usually performed using first and second derivatives. Therefore, edge detection in an image can be determined by deriving the gray value, and the derivative operation can be performed by a differential operator;
the Sobel operator is a first order differential operator, which calculates the gradient of each pixel by the gradient value of the adjacent area of the pixel, and finally performs the selection according to a fixed threshold value. The calculation formula is as follows:
the Sobel operator is a three-layer operator template, and is formed by two convolution kernels of dx and dy. One convolution kernel carries out calculation of a vertical edge, one convolution kernel carries out calculation of a horizontal convolution kernel, and the maximum value of the two convolution kernels is used as the final output result of the calculation.
In addition to the Sobel operator, Canny operator is also used herein for edge detection. The Canny operator is used herein to calculate local maxima of the image gradient, finding strong and weak edges of the target by two thresholds. The method is essentially to carry out smoothing treatment through a quasi-Gaussian function, and then locate the maximum value of the derivative through a first-order differential operator. The mean of the finite differences can be calculated in the second order square to find the partial derivative gradient at a point in the image. The direction angle and the amplitude can be calculated by a coordinate conversion formula from a rectangular coordinate system to a polar coordinate system:
where M [ i, j ] reflects the edge strength of the image and θ [ i, j ] reflects the edge direction of the image. When M [ i, j ] obtains a local maximum, θ [ i, j ] is the edge direction at that time. For the gradient amplitude, extracting a pixel with the maximum gradient value in each gradient direction by adopting a non-maximum value inhibition method;
2.2) detecting the front edge of the part, wherein the detection process is as shown in fig. 3, and because too much front edge information can seriously affect the classification result, the invention provides a method for eliminating redundant edge information, which removes high-frequency signals in an image, and comprises the following steps:
2.2.1) preprocessing the image by using smooth filtering to remove high-frequency signals in the image;
2.2.2) detecting the image edge information by adopting an edge detection method and extracting a closed contour of the image;
2.2.3) carrying out ellipse fitting on the outline by using an ellipse fitting method, wherein the ellipse fitting is carried out because the target area to be extracted is a circular ring;
2.2.4) selecting a proper threshold value according to the fixed information of the image, screening the obtained elliptic contour to obtain the inner circle contour and the outer circle contour of the circular ring area, and finally obtaining the target area to be extracted by adopting an image subtraction method.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent structures or equivalent flow transformations made by using the contents of the specification and drawings, or applied directly or indirectly to other related technical fields are included in the scope of the present invention.
Claims (3)
1. A bearing steel ball part detection method based on machine vision is characterized by comprising the following steps:
the method comprises the following steps of firstly, detecting the circularity of a steel ball, and the process is as follows:
1.1) image preprocessing: preprocessing an input image by using Gaussian filtering to remove high-frequency signals in the image;
1.2) acquiring the steel ball profile: the image is converted by methods such as gray level conversion, threshold segmentation and the like, so that the subsequent algorithm can more easily identify image information; through this step, the system will get several closed edge profiles;
1.3) screening edge profiles: in order to obtain a better detection edge, three screening conditions are adopted: contour pixel domain detection, ellipse fitting detection and circularity detection, wherein the contour pixel domain detection is to count the pixel area of each contour; because the circular outline of the steel ball can form an ellipse similar to a circle after imaging, ellipse fitting is carried out, and noise interference can be effectively eliminated; finally, after the edge profile information of the steel ball is obtained, the circularity of the ellipse is detected, and the purpose of the step is to detect whether the shape of the steel ball meets the production specification;
secondly, detecting scratches of the parts, wherein the process is as follows:
2.1) detecting scratches on the side surfaces of the parts: detecting the scratches on the side surfaces of the parts by using an edge detection method, firstly determining edge pixels in an image, and then connecting the pixels together to form a required area boundary;
2.2) detecting scratches on the front surface of the part: because too much edge information on the front side can cause serious influence on the classification result, a method for eliminating redundant edge information is adopted to remove high-frequency signals in the image.
2. The method for detecting a steel ball part of a bearing based on machine vision as claimed in claim 1, wherein in 2.1), the image edge represents the beginning of one region and the other region in the image, and the set of pixels between adjacent regions in the image forms the edge of the image, so that the edge of the image can be understood as the set of pixels with spatial abrupt change of the gray level of the image, and the image edge has two important concepts: the direction and gradient of the pixel change along the edge direction are smooth, and the pixel change perpendicular to the edge direction is severe, so according to the characteristic, the edge detection is usually performed by using a first derivative and a second derivative, so that the edge detection in an image can be determined by differentiating the gray value, and the derivative operation can be performed by a differential operator.
3. A bearing steel ball part detection method based on machine vision as claimed in claim 1 or 2, characterized in that the steps of 2.2) are as follows:
2.2.1) preprocessing the image by using smooth filtering to remove high-frequency signals in the image;
2.2.2) detecting the image edge information by adopting an edge detection method and extracting a closed contour of the image;
2.2.3) carrying out ellipse fitting on the outline by using an ellipse fitting method, wherein the ellipse fitting is carried out because the target area to be extracted is a circular ring;
2.2.4) selecting a proper threshold value according to the fixed information of the image, screening the obtained elliptic contour to obtain the inner circle contour and the outer circle contour of the circular ring area, and finally obtaining the target area to be extracted by adopting an image subtraction method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010564947.0A CN111815575B (en) | 2020-06-19 | 2020-06-19 | Bearing steel ball part detection method based on machine vision |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010564947.0A CN111815575B (en) | 2020-06-19 | 2020-06-19 | Bearing steel ball part detection method based on machine vision |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111815575A true CN111815575A (en) | 2020-10-23 |
| CN111815575B CN111815575B (en) | 2024-03-26 |
Family
ID=72846239
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010564947.0A Active CN111815575B (en) | 2020-06-19 | 2020-06-19 | Bearing steel ball part detection method based on machine vision |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111815575B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613523A (en) * | 2020-12-15 | 2021-04-06 | 中冶赛迪重庆信息技术有限公司 | Method, system, medium and electronic terminal for identifying steel flow at converter steel tapping hole |
| CN116363136A (en) * | 2023-06-01 | 2023-06-30 | 山东创元智能设备制造有限责任公司 | On-line screening method and system for automatic production of motor vehicle parts |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103175844A (en) * | 2012-03-16 | 2013-06-26 | 沈阳理工大学 | Detection method for scratches and defects on surfaces of metal components |
| CN106053485A (en) * | 2016-08-01 | 2016-10-26 | 苏州宙点自动化设备有限公司 | Machine vision-based novel algorithm of intelligent circular inspection of steel ball surface defects |
| CN109003258A (en) * | 2018-06-15 | 2018-12-14 | 广东工业大学 | A kind of high-precision sub-pix circular pieces measurement method |
| CN111862037A (en) * | 2020-07-17 | 2020-10-30 | 华中科技大学无锡研究院 | Method and system for detecting geometric characteristics of precision hole type part based on machine vision |
-
2020
- 2020-06-19 CN CN202010564947.0A patent/CN111815575B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103175844A (en) * | 2012-03-16 | 2013-06-26 | 沈阳理工大学 | Detection method for scratches and defects on surfaces of metal components |
| CN106053485A (en) * | 2016-08-01 | 2016-10-26 | 苏州宙点自动化设备有限公司 | Machine vision-based novel algorithm of intelligent circular inspection of steel ball surface defects |
| CN109003258A (en) * | 2018-06-15 | 2018-12-14 | 广东工业大学 | A kind of high-precision sub-pix circular pieces measurement method |
| CN111862037A (en) * | 2020-07-17 | 2020-10-30 | 华中科技大学无锡研究院 | Method and system for detecting geometric characteristics of precision hole type part based on machine vision |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613523A (en) * | 2020-12-15 | 2021-04-06 | 中冶赛迪重庆信息技术有限公司 | Method, system, medium and electronic terminal for identifying steel flow at converter steel tapping hole |
| CN112613523B (en) * | 2020-12-15 | 2023-04-07 | 中冶赛迪信息技术(重庆)有限公司 | Method, system, medium and electronic terminal for identifying steel flow at converter steel tapping hole |
| CN116363136A (en) * | 2023-06-01 | 2023-06-30 | 山东创元智能设备制造有限责任公司 | On-line screening method and system for automatic production of motor vehicle parts |
| CN116363136B (en) * | 2023-06-01 | 2023-08-11 | 山东创元智能设备制造有限责任公司 | On-line screening method and system for automatic production of motor vehicle parts |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111815575B (en) | 2024-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109490316B (en) | Surface defect detection algorithm based on machine vision | |
| CN109003258B (en) | High-precision sub-pixel circular part measuring method | |
| CN109141232B (en) | Online detection method for disc castings based on machine vision | |
| CN108921176B (en) | Pointer instrument positioning and identifying method based on machine vision | |
| CN105334219B (en) | A kind of bottle mouth defect detection method of residual analysis dynamic threshold segmentation | |
| CN109682839B (en) | Online detection method for surface defects of metal arc-shaped workpiece | |
| CN102496161B (en) | Method for extracting contour of image of printed circuit board (PCB) | |
| CN107796826B (en) | Micro duplicate gear broken tooth defect detection method based on tooth center distance curve analysis | |
| CN112686920A (en) | Visual measurement method and system for geometric dimension parameters of circular part | |
| CN108876860B (en) | Image calibration method for measuring bubble offset of tube level | |
| CN107945155B (en) | Toothpaste tube shoulder defect detection method based on Gabor filter | |
| CN109115800B (en) | Method for rapidly detecting burrs of product and accurately measuring length | |
| CN112001917A (en) | Machine vision-based geometric tolerance detection method for circular perforated part | |
| CN107742289A (en) | One kind is based on machine vision revolving body workpieces detection method | |
| CN109658402A (en) | Industry profile geometric dimension automatic testing method based on computer vision imaging | |
| CN111815575B (en) | Bearing steel ball part detection method based on machine vision | |
| CN113689415A (en) | Steel pipe wall thickness online detection method based on machine vision | |
| CN114279357A (en) | Die casting burr size measurement method and system based on machine vision | |
| CN114881915A (en) | Symmetry-based mobile phone glass cover plate window area defect detection method | |
| CN115375686A (en) | Glass edge flaw detection method based on image processing | |
| CN105184792B (en) | A kind of saw blade wear extent On-line Measuring Method | |
| CN113847881A (en) | Free-form surface profile tolerance detection method based on machine vision | |
| CN115100191A (en) | Metal casting defect identification method based on industrial detection | |
| CN116539620A (en) | On-machine detection method for surface defects of cutter | |
| CN112014407A (en) | Method for detecting surface defects of integrated circuit wafer |
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 |