CN111681241A - Quality control method and system based on machine vision detection and measurement depth integration - Google Patents
Quality control method and system based on machine vision detection and measurement depth integration Download PDFInfo
- Publication number
- CN111681241A CN111681241A CN202010805493.1A CN202010805493A CN111681241A CN 111681241 A CN111681241 A CN 111681241A CN 202010805493 A CN202010805493 A CN 202010805493A CN 111681241 A CN111681241 A CN 111681241A
- Authority
- CN
- China
- Prior art keywords
- coating
- quality control
- machine vision
- coating area
- camera
- 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
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
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- 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
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8854—Grading and classifying of flaws
- G01N2021/8874—Taking dimensions of defect into account
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Geometry (AREA)
- Signal Processing (AREA)
- Quality & Reliability (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The invention relates to a quality control method and a system based on machine vision detection and measurement depth integration. The invention aims to provide a quality control method and a quality control system based on machine vision detection and measurement depth integration. The technical scheme of the invention is as follows: a quality control method based on machine vision detection and measurement depth integration is characterized in that: s1, acquiring images of the upper surface and the lower surface of the material; s2, dividing the material image into a coating area and a non-coating area based on the image gray value; s3, identifying defects of the coating area and the non-coating area; measuring the relative size of the coating on the obtained material image; s4, judging whether the material is qualified or not based on the defect characteristics of the defects of the coating area and the non-coating area and the relevant coating size; and if the material is not qualified, triggering an alarm and/or sending the unqualified reason to a material production control system to provide closed-loop control feedback for the material production control system. The invention is suitable for the field of universal visual detection equipment.
Description
Technical Field
The invention relates to a quality control method and a system based on machine vision detection and measurement depth integration. The method is suitable for the field of universal visual detection equipment.
Background
With the continuous reduction of fossil energy and the more serious pollution brought to the environment by the use of fossil energy, the lithium battery has the advantages of being green, high in energy density, capable of being charged circularly and the like as clean energy, and is widely applied to the field of electric vehicles.
In the coating production and manufacturing process of the lithium battery, the defects on the surface of the electrode and the poor coating size can greatly affect the subsequent welding process and the service life of the battery, and even have a relation with the safety problem of the electric automobile under extreme conditions, so that the defects and the size parameters need to be monitored in real time in the production and manufacturing process, the product quality problem can be found in advance, and unnecessary scrapping is avoided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in view of the above problems, a quality control method and system based on machine vision inspection and depth measurement integration are provided.
The technical scheme adopted by the invention is as follows: a quality control method based on machine vision detection and measurement depth integration is characterized in that:
s1, acquiring images of the upper surface and the lower surface of the material;
s2, dividing the material image into a coating area and a non-coating area based on the image gray value;
s3, identifying defects of the coating area and the non-coating area; measuring the relative size of the coating on the obtained material image;
s4, judging whether the material is qualified or not based on the defect characteristics of the defects of the coating area and the non-coating area and the relevant coating size;
and if the material is not qualified, triggering an alarm and/or sending the unqualified reason to a material production control system to provide closed-loop control feedback for the material production control system.
Step S2 includes:
positioning a ROI (region of interest) in a detection area through an image gray value according to the condition that an aluminum foil substrate on a material is white and the aluminum foil substrate is coated to be black;
the longitudinal boundaries of the coating area and the non-coating area are determined in a global search edge finding and linear fitting mode in the material advancing direction, the transverse boundaries of the coating area are identified through an edge finding tool, and the image is divided into the coating area and the non-coating area.
Step S3 includes: the coated area and the non-coated area are subjected to image preprocessing and a convolutional neural network algorithm to identify defective pixels.
The defect characteristics comprise defect length, width, gray value, morphological characteristics and gray range.
The relevant dimensions of the coating include coating width, left margin width, right margin width, coating segment length, margin length, tail length, and upper and lower coating misalignment data.
And calculating the upper and lower coating dislocation data based on the combination position deviation value of the images of the upper surface and the lower surface of the material.
A quality control system based on machine vision inspection and metrology depth integration, comprising:
the camera I faces the upper surface of the material and is used for collecting an image of the upper surface of the material;
the light source I is used for illuminating the corresponding part of the camera I on the material;
the camera II faces the upper surface of the material and is used for collecting an image of the upper surface of the material;
the light source II is used for illuminating the corresponding part of the camera I on the material;
the device comprises an encoder, a camera and a controller, wherein a roller of the encoder is in contact with a material and is used for acquiring the advancing length of the material and providing an image acquisition trigger signal for the cameras I and II after the material advances for a certain length;
and the upper computer is provided with a processor and a memory, and the memory is stored with a computer program which realizes the steps of the quality control method based on machine vision detection and depth measurement integration when being executed by the processor.
The position deviation value is determined based on the position relation between the camera I and the camera II.
A lower surface detection roller, a driving roller and an upper surface detection roller are sequentially arranged along the advancing direction of the material, the lower surface of the material wound on the lower surface detection roller faces outwards, and the upper surface of the material wound on the upper surface detection roller faces outwards;
the camera I faces to the material positioned on the upper surface detection roller, and the camera II faces to the material positioned on the lower surface detection roller.
And the roller of the encoder is matched with the driving roller to compress materials.
The invention has the beneficial effects that: the invention divides the material image into a coating area and a non-coating area, detects the defects in different areas, measures and obtains the relevant size of the coating based on the divided coating area and non-coating area, and judges whether the material is qualified according to the defects and the coating size. The invention simultaneously monitors the defects and the measured data in real time, provides multidimensional more comprehensive quality data for a production line and ensures that the produced products are more reliable.
Drawings
Fig. 1 is a schematic structural diagram of a control system in an embodiment.
Fig. 2 and 3 are schematic diagrams of coating-related dimensions in the examples.
FIG. 4 is a flow chart of the system in an embodiment.
In the figure: 1. a light source I; 2. a camera I; 3. a lithium battery motor material; 4. an upper surface detection roller; 5. a drive roll; 6. an encoder; 7. a camera II; 8. a light source II; 9. and a lower surface detection roller.
Detailed Description
As shown in fig. 1, the present embodiment is a quality control system based on machine vision detection and depth measurement integration, and has a lower surface detection roller 9, a drive roller 5 and an upper surface detection roller 4 for conveying a lithium battery motor material 3, where the lower surface detection roller 9, the drive roller 5 and the upper surface detection roller 4 are sequentially arranged along a traveling direction of the lithium battery motor material 3, a lower surface of a material wound on the lower surface detection roller 9 faces outward, an upper surface of the material wound on the drive roller 5 faces outward, and an upper surface of the material wound on the upper surface detection roller 4 faces outward.
In the embodiment, the quality control system is provided with an upper computer, a camera I, a camera II (8K pixel industrial grade line scanning camera light distribution optical lens), a light source I, a light source II (LED linear light source) and an encoder 6, wherein the camera I2 faces to a material positioned at the upper surface detection roller 4 to collect an image of the upper surface of the material; detecting the material at the position of the roller 9 on the lower surface of the material, which faces the position by the camera II 7, and acquiring an image of the lower surface of the material; light source I1, light source II 8 are camera I2, camera II 7 collection image respectively and provide sufficient illumination.
In this embodiment, the encoder 6 is installed on the periphery of the driving roll 5, the roller on the encoder 6 and the driving roll 5 are matched to compress the material, and the material can drive the roller on the encoder 6 to rotate when advancing, so that the advancing length of the material can be obtained through the encoder 6. After the material advances for a certain length, the encoder 6 provides image acquisition trigger signals for the cameras I and II, and the cameras I and II receive the image acquisition trigger signals to start to acquire images.
As shown in fig. 4, the upper computer in this embodiment has a processor and a memory, and the memory stores a computer program, and when the computer program is executed by the processor, the following steps of the detection method are implemented:
s1, acquiring images of the upper surface and the lower surface of the material acquired by the cameras I and II;
s2, positioning a region for detection ROI (region of interest) according to the gray value of an image, namely white aluminum foil base material and black aluminum foil base material coated on the material, determining the longitudinal boundaries of a coated region and a non-coated region in a global search edge finding and linear fitting mode in the advancing direction of the material, identifying the transverse boundary of the coated region by an edge finding tool, and dividing the image into the coated region and the non-coated region;
and S3, identifying the defects of the coating area and the non-coating area, and measuring the relevant size coated on the acquired material image.
The defect detection and the size measurement are distributed to different threads to be executed synchronously, the current piece is detected, meanwhile, the image of the next piece is transmitted to the cache space of the upper computer through the acquisition board card, when the image acquisition of the next piece is completed, the upper computer software is triggered through an event to start the detection of the next piece, and the steps are repeated in such a cycle. In the working process of the upper computer, a plurality of tasks without conflict relation can be operated in parallel, so that the software running efficiency can be greatly improved, and the defect detection and the size measurement can be finished in the same system.
And (3) defect detection: and identifying defective pixels in the coating area and the non-coating area through image preprocessing and a convolutional neural network algorithm to form a defective picture. Classifying the defects according to the characteristics of the length, the width, the gray value, the morphological characteristics, the gray range and the like of the defects, grading according to the severity grade of the defects, and judging the defects to be unqualified products if the number of some serious defects in the unit length exceeds a set threshold value.
Relevant dimensions for coating include coating width L2, left margin width L1, right margin width L3, coating segment length L4, margin length L5, tail length L6, and upper and lower coating misalignment data (see fig. 2), where the distance between the left and right boundaries of the coating region is calculated as the coating width, the distance between the left boundary of the coating region and the left boundary of the foil is calculated as the left margin width, the distance between the right boundary of the coating region and the right boundary of the foil is calculated as the right margin width, the distance between the upper and lower boundaries of the coating region is calculated as the coating segment length, the distance between the upper and lower boundaries of the coating region is calculated as the margin length, the die in normal processes will have ragged residue at the end of each piece of coating, and the distance from the end of coating to the tail boundary is calculated as the tail length, all of the above dimensions being applicable to both upper and lower surface products.
In the present embodiment, the coating misalignment data is analyzed in conjunction with the images of the upper and lower surfaces, and the difference in position between the upper boundary of the upper surface coating region and the upper boundary of the lower surface coating region is calculated as the head misalignment L7, and the difference in position between the lower boundary of the upper surface coating region and the lower boundary of the lower surface coating region is calculated as the tail misalignment L8 (see fig. 3). The upper surface detection work station and the lower surface detection work station are arranged at different positions due to space problems, so that the same pole piece is detected by the upper surface camera and the lower surface camera in different time, error bit data are calculated by compensating corresponding position deviation values, and the position deviation values are determined based on the position relation of the camera I2 and the camera II 7.
S5, judging that any one of unqualified products or dimension measurement in defect detection exceeds a tolerance range and is regarded as an unqualified product, sending a bad signal to the PLC, triggering an alarm lamp and a buzzer by the PLC through an IO port, confirming by a field operator and improving process parameters according to the result; the size deviation data can also be directly sent to a material production control system PLC through a TCP/IP protocol, and the material production control system PLC receives the data and then realizes closed-loop control of quality by automatically adjusting coating parameters.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A quality control method based on machine vision detection and measurement depth integration is characterized in that:
s1, acquiring images of the upper surface and the lower surface of the material;
s2, dividing the material image into a coating area and a non-coating area based on the image gray value;
s3, identifying defects of the coating area and the non-coating area; measuring the relative size of the coating on the obtained material image;
s4, judging whether the material is qualified or not based on the defect characteristics of the defects of the coating area and the non-coating area and the relevant coating size;
and if the material is not qualified, triggering an alarm and/or sending the unqualified reason to a material production control system to provide closed-loop control feedback for the material production control system.
2. The quality control method based on machine vision inspection and integration of metrology depth as claimed in claim 1, wherein step S2 comprises:
positioning a ROI (region of interest) in a detection area through an image gray value according to the condition that an aluminum foil substrate on a material is white and the aluminum foil substrate is coated to be black;
the longitudinal boundaries of the coating area and the non-coating area are determined in a global search edge finding and linear fitting mode in the material advancing direction, the transverse boundaries of the coating area are identified through an edge finding tool, and the image is divided into the coating area and the non-coating area.
3. The quality control method based on machine vision inspection and integration of metrology depth as claimed in claim 1, wherein step S3 comprises: the coated area and the non-coated area are subjected to image preprocessing and a convolutional neural network algorithm to identify defective pixels.
4. The quality control method based on machine vision inspection and metrology depth integration of claim 1 or 3, wherein: the defect characteristics comprise defect length, width, gray value, morphological characteristics and gray range.
5. The quality control method based on machine vision inspection and metrology depth integration of claim 1, wherein: the relevant dimensions of the coating include coating width, left margin width, right margin width, coating segment length, margin length, tail length, and upper and lower coating misalignment data.
6. The quality control method based on machine vision inspection and metrology depth integration of claim 5, wherein: and calculating the upper and lower coating dislocation data based on the combination position deviation value of the images of the upper surface and the lower surface of the material.
7. A quality control system based on machine vision inspection and metrology depth integration, comprising:
the camera I faces the upper surface of the material and is used for collecting an image of the upper surface of the material;
the light source I is used for illuminating the corresponding part of the camera I on the material;
the camera II faces the upper surface of the material and is used for collecting an image of the upper surface of the material;
the light source II is used for illuminating the corresponding part of the camera I on the material;
the device comprises an encoder, a camera and a controller, wherein a roller of the encoder is in contact with a material and is used for acquiring the advancing length of the material and providing an image acquisition trigger signal for the cameras I and II after the material advances for a certain length;
an upper computer having a processor and a memory, the memory having a computer program stored thereon, the computer program, when executed by the processor, implementing the steps of the quality control method based on machine vision inspection and integration of depth measurement according to any one of claims 1 to 6.
8. The integrated quality control system based on machine vision inspection and metrology depth of claim 7, wherein: the position deviation value is determined based on the position relation between the camera I and the camera II.
9. The integrated quality control system based on machine vision inspection and metrology depth of claim 7, wherein: a lower surface detection roller, a driving roller and an upper surface detection roller are sequentially arranged along the advancing direction of the material, the lower surface of the material wound on the lower surface detection roller faces outwards, and the upper surface of the material wound on the upper surface detection roller faces outwards;
the camera I faces to the material positioned on the upper surface detection roller, and the camera II faces to the material positioned on the lower surface detection roller.
10. The machine-vision-based inspection and metrology depth integration quality control system of claim 9, wherein: and the roller of the encoder is matched with the driving roller to compress materials.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010805493.1A CN111681241A (en) | 2020-08-12 | 2020-08-12 | Quality control method and system based on machine vision detection and measurement depth integration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010805493.1A CN111681241A (en) | 2020-08-12 | 2020-08-12 | Quality control method and system based on machine vision detection and measurement depth integration |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111681241A true CN111681241A (en) | 2020-09-18 |
Family
ID=72458324
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010805493.1A Pending CN111681241A (en) | 2020-08-12 | 2020-08-12 | Quality control method and system based on machine vision detection and measurement depth integration |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111681241A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112215825A (en) * | 2020-10-19 | 2021-01-12 | 杭州百子尖科技股份有限公司 | Quality analysis method and system based on machine vision in new energy battery manufacturing |
| CN115106257A (en) * | 2022-06-22 | 2022-09-27 | 广东聚德机械有限公司 | Coating machine and corresponding coating method |
| CN115290677A (en) * | 2022-08-03 | 2022-11-04 | 广东聚德机械有限公司 | Blank detection method and coating system for base material |
| CN115619779A (en) * | 2022-12-14 | 2023-01-17 | 杭州百子尖科技股份有限公司 | Hot melt adhesive box sealing machine vision detection system and method based on thermal imaging |
| WO2024000514A1 (en) * | 2022-06-30 | 2024-01-04 | 宁德时代新能源科技股份有限公司 | Electrode plate test apparatus, method and system |
| CN117686497A (en) * | 2024-02-01 | 2024-03-12 | 杭州百子尖科技股份有限公司 | Machine vision-based diaphragm spraying detection method and device, electronic equipment and medium |
| CN117740828A (en) * | 2024-02-20 | 2024-03-22 | 宁德时代新能源科技股份有限公司 | Encapsulation detection system and encapsulation detection method for cylindrical battery cell |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0762740A2 (en) * | 1995-08-29 | 1997-03-12 | British Broadcasting Corporation | Blemish concealment in video signals |
| CN102854191A (en) * | 2012-07-18 | 2013-01-02 | 湖南大学 | Real-time visual detection and identification method for high speed rail surface defect |
| CN105911070A (en) * | 2016-05-03 | 2016-08-31 | 深圳市新嘉拓自动化技术有限公司 | Coating visual inspection control device and inspection control method |
| CN109324060A (en) * | 2018-10-11 | 2019-02-12 | 广东德尔智慧工厂科技有限公司 | A kind of lithium electropaining cloth surface defects detection system |
| CN109636717A (en) * | 2018-10-30 | 2019-04-16 | 广州超音速自动化科技股份有限公司 | Lithium battery is coated with determination method, system, equipment and storage medium |
| CN111398290A (en) * | 2020-03-20 | 2020-07-10 | 华中科技大学 | Coating liquid bead defect identification method suitable for slit coating |
-
2020
- 2020-08-12 CN CN202010805493.1A patent/CN111681241A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0762740A2 (en) * | 1995-08-29 | 1997-03-12 | British Broadcasting Corporation | Blemish concealment in video signals |
| CN102854191A (en) * | 2012-07-18 | 2013-01-02 | 湖南大学 | Real-time visual detection and identification method for high speed rail surface defect |
| CN105911070A (en) * | 2016-05-03 | 2016-08-31 | 深圳市新嘉拓自动化技术有限公司 | Coating visual inspection control device and inspection control method |
| CN109324060A (en) * | 2018-10-11 | 2019-02-12 | 广东德尔智慧工厂科技有限公司 | A kind of lithium electropaining cloth surface defects detection system |
| CN109636717A (en) * | 2018-10-30 | 2019-04-16 | 广州超音速自动化科技股份有限公司 | Lithium battery is coated with determination method, system, equipment and storage medium |
| CN111398290A (en) * | 2020-03-20 | 2020-07-10 | 华中科技大学 | Coating liquid bead defect identification method suitable for slit coating |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112215825A (en) * | 2020-10-19 | 2021-01-12 | 杭州百子尖科技股份有限公司 | Quality analysis method and system based on machine vision in new energy battery manufacturing |
| CN115106257A (en) * | 2022-06-22 | 2022-09-27 | 广东聚德机械有限公司 | Coating machine and corresponding coating method |
| WO2024000514A1 (en) * | 2022-06-30 | 2024-01-04 | 宁德时代新能源科技股份有限公司 | Electrode plate test apparatus, method and system |
| CN115290677A (en) * | 2022-08-03 | 2022-11-04 | 广东聚德机械有限公司 | Blank detection method and coating system for base material |
| CN115290677B (en) * | 2022-08-03 | 2023-08-22 | 广东聚德机械有限公司 | Substrate blank detection method and coating system |
| CN115619779A (en) * | 2022-12-14 | 2023-01-17 | 杭州百子尖科技股份有限公司 | Hot melt adhesive box sealing machine vision detection system and method based on thermal imaging |
| CN117686497A (en) * | 2024-02-01 | 2024-03-12 | 杭州百子尖科技股份有限公司 | Machine vision-based diaphragm spraying detection method and device, electronic equipment and medium |
| CN117740828A (en) * | 2024-02-20 | 2024-03-22 | 宁德时代新能源科技股份有限公司 | Encapsulation detection system and encapsulation detection method for cylindrical battery cell |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111681241A (en) | Quality control method and system based on machine vision detection and measurement depth integration | |
| CN111537517B (en) | Unmanned intelligent stamping defect identification method | |
| CN107907063B (en) | Steel strip punching processing detection system and method based on vision measurement | |
| EP1995553B1 (en) | System and method for identifying a feature of a workpiece | |
| CN111929314A (en) | Wheel hub weld visual detection method and detection system | |
| CN104614380A (en) | Plate-strip surface quality detection system and method | |
| CN116718616B (en) | Machine vision detection system and detection method for flaw detection | |
| CN109030384B (en) | Method for monitoring polishing quality on line | |
| CN109693140B (en) | Intelligent flexible production line and working method thereof | |
| CN113325001A (en) | Automatic distinguishing and detecting equipment and method for surface appearance flaws of copper foil | |
| CN115272658A (en) | Copper foil defect detection method, system, equipment and storage medium | |
| CN112215825A (en) | Quality analysis method and system based on machine vision in new energy battery manufacturing | |
| CN117086494A (en) | Die-cutting and slitting system and visual detection method for die-cutting and slitting | |
| CN117073534A (en) | Glue spreading detection method and pole piece glue spreading system | |
| CN107797517B (en) | Method and system for realizing steel belt punching processing detection by adopting machine vision | |
| CN114529510B (en) | Automatic detection and classification method for cathode copper on-line quality | |
| CN108375586B (en) | Defect detection device with multiple detection modes and method thereof | |
| CN111207304B (en) | Railway tunnel leaky cable vision inspection device and product positioning detection method | |
| CN108732148B (en) | Online detection device and method for fluorescent magnetic particle inspection | |
| CN111208146A (en) | Tunnel cable detection system and detection method | |
| CN113641155A (en) | High-temperature forging detection control system | |
| CN116664508A (en) | Weld surface quality detection method and computer readable storage medium | |
| CN116060317A (en) | Weld pool defect detection and position measurement mechanism and detection method thereof | |
| CN116078704A (en) | Appearance detection equipment based on CCD | |
| CN113305017B (en) | Comprehensive intelligent detection and sorting method for full-automatic valve element |
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: 20200918 |
|
| RJ01 | Rejection of invention patent application after publication |