CN107356207A - A kind of the winged of view-based access control model identification knits vamp deformation detection method - Google Patents
A kind of the winged of view-based access control model identification knits vamp deformation detection method Download PDFInfo
- Publication number
- CN107356207A CN107356207A CN201710502157.8A CN201710502157A CN107356207A CN 107356207 A CN107356207 A CN 107356207A CN 201710502157 A CN201710502157 A CN 201710502157A CN 107356207 A CN107356207 A CN 107356207A
- Authority
- CN
- China
- Prior art keywords
- mrow
- mark point
- vamp
- deformation
- msub
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
-
- 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
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Cosmetics (AREA)
Abstract
The present invention by fly to knit vamp treat the mark point with contrast is set on cut-out, and combine the positional information that Visual identification technology obtains mark point, obtain vamp stretcher strain amount and torsional deflection amount according to the positional information combination threshold process of mark point, judge stretcher strain amount and torsional deflection amount whether respectively fall in stretcher strain amount threshold range and torsional deflection amount threshold range can determine whether to fly to knit vamp it is whether qualified.For artificial detection, the detection method can realize automatic detection, and detection efficiency is high, eliminate the influence of human factor, and the stability of detection is high.
Description
Technical field
The present invention relates to a kind of deformation detection method for flying to knit vamp, and in particular to a kind of the winged of view-based access control model identification knits footwear
Facial disfigurement detection method.
Background technology
It is disposably to knit out the fabric of a shoes to fly to knit vamp technology, substitutes multi-panel sewing machine technique.This
Sample greatly increases the production efficiency of vamp, but knits out the vamp come it is difficult to ensure that the stability of its shape, it may occur that
Deformation.Scene needs to detect decorative pattern deflection on vamp after calendaring technology sizing, thus determines whether certified products, such as
If deformation, which will pass through greatly very much the flat technique of soup, corrects deformation.Before this technological invention, the deformation detection for flying to knit vamp is to use people
Work is low to the mode of film original text decorative pattern, the slow efficiency of speed.
The content of the invention
Winged it is an object of the invention to provide a kind of identification of view-based access control model knits vamp deformation detection method, and it is by flying
Increase specific markers point is knitted on vamp, the positional information line position of going forward side by side that mark point is obtained with reference to Visual identification technology is put deformation and judged
Judge with torsional deflection, it is whether qualified so as to judge to fly to knit vamp.
To achieve the above object, the technical solution adopted by the present invention is：
A kind of the winged of view-based access control model identification knits vamp deformation detection method, and it is by flying to knit the treating on cut-out of vamp
Mark point with contrast is set, and combines the positional information that Visual identification technology obtains mark point, the position according to mark point
Confidence breath obtains vamp stretcher strain amount and torsional deflection amount with reference to threshold process, judges that stretcher strain amount and torsional deflection amount are
It is no respectively fall in stretcher strain amount threshold range and torsional deflection amount threshold range can determine whether to fly to knit vamp it is whether qualified.
The detection method specifically includes following steps：
Step 1, fly to knit vamp treat to set the mark point with contrast on cut-out, the mark point includes one
Tagging point, a size-mark point and at least one set of deformation mark point；
The datum mark that the position mark point identifies as vamp, it is arranged in vamp symmetrical center line and positioned at whole
The centre of area point region of vamp；
The size-mark point is arranged in vamp symmetrical center line close to the opening position of top；
One group of deformation mark point is made up of two deformation mark points for being separately positioned on vamp both sides；
Step 2, by camera fly to knit vamp picture to flying to knit the vamp acquisition that take pictures, then in conjunction with Visual identification technology
The position of position mark point, size-mark point and deformation mark point is obtained in flying to knit vamp picture；
Step 3, open up multiple threads an information processing is marked, wherein,
First thread is used to calculate stretching distance BXi, i=1,2 ..., 3n and windup-degree ∠ θi, i=1,2 ...,
2n, n are the group number of deformation mark point；The stretching distance be in every group of deformation mark point the distance between 2 deformation mark points with
And the distance of deformation mark point and position mark point, windup-degree are the line and center line of deformation mark point and position mark point
Angle；
Second thread is used to calculate the distance between size-mark point and position mark point to obtain corresponding size, according to this
Size, which is transferred, flies the preferable stretching distance BX for knitting vampi', i=1,2 ..., 3n, preferable windup-degree ∠ θi', i=1,2 ...,
2n and stretcher strain amount threshold range Δ Xmin~Δ Xmax and torsional deflection amount threshold range Δ ∠ min~Δ ∠ max；
Step 4, stretcher strain amount and torsional deflection amount are calculated, stretcher strain amount is what first thread obtained
The absolute value of difference between the preferable stretching distance that stretching distance and the second thread obtain, i.e.,
ΔBXi=| BXi-BX′i|, i=1,2 ..., 3n
The absolute value of difference of the torsional deflection amount between the windup-degree arrived of first thread and preferable windup-degree, i.e.,
Δ∠θi=| ∠ θi-∠θi' |, i=1,2 ..., 2n
Step 5, comprehensive deformation amount is sought using weighted average method, must stretch comprehensive deformation amount is：
Reversing comprehensive deformation amount is：
WXiFor Δ BXiWeight coefficient, W ∠iFor Δ ∠iWeight coefficient；
Step 6, judge to stretch comprehensive deformation amount whether in stretcher strain amount threshold range, and reverse comprehensive deformation amount
Whether in torsional deflection amount threshold range, only when stretching comprehensive deformation amount and reverse comprehensive deformation amount fall into threshold range,
It is just qualified products to fly to knit vamp, is otherwise substandard product.
For artificial detection, the detection method can realize automatic detection, and detection efficiency is high, eliminates human factor
Influence, the stability of detection is high.
Brief description of the drawings
Fig. 1 is overhaul flow chart of the present invention；
Fig. 2 flies to knit the mark point schematic diagram of vamp for the present invention.
Embodiment
Winged present invention is disclosed a kind of identification of view-based access control model knits vamp deformation detection method, and it comprises the following steps：
Step 1, fly to knit vamp treat to set the mark point with contrast on cut-out, the mark point includes one
Tagging point, a size-mark point and at least one set of deformation mark point；
The datum mark that the position mark point identifies as vamp, it is arranged in vamp symmetrical center line and positioned at whole
The centre of area region of vamp；
The size-mark point is arranged in vamp symmetrical center line close to the opening position of toe cap；
One group of deformation mark point is made up of two deformation mark points for being separately positioned on vamp both sides；
Step 2, by camera fly to knit vamp picture to flying to knit the vamp acquisition that take pictures, then in conjunction with Visual identification technology
The position of position mark point, size-mark point and deformation mark point is obtained in flying to knit vamp picture；
Step 3, open up multiple threads an information processing is marked, wherein,
First thread is used to calculate stretching distance BXi, i=1,2 ..., 3n and windup-degree ∠ θi, i=1,2 ...,
2n, n are the group number of deformation mark point；The stretching distance be in every group of deformation mark point the distance between 2 deformation mark points with
And the distance of deformation mark point and position mark point, windup-degree are the line and center line of deformation mark point and position mark point
Angle；
Second thread is used to calculate the distance between size-mark point and position mark point to obtain corresponding size, according to this
Size, which is transferred, flies the preferable stretching distance BX for knitting vampi', i=1,2 ..., 3n, preferable windup-degree ∠ θi', i=1,2 ...,
2n and stretcher strain amount threshold range Δ Xmin~Δ Xmax and torsional deflection amount threshold range Δ ∠ min~Δ ∠ max；
Step 4, calculate stretcher strain amount and torsional deflection amount, stretcher strain amount be the stretching distance that first thread obtains and
The absolute value of difference between the preferable stretching distance that second thread obtains, i.e.,
ΔBXi=| BXi-BX′i|, i=1,2 ..., 3n
The absolute value of difference of the torsional deflection amount between the windup-degree arrived of first thread and preferable windup-degree, i.e.,
Δ∠θi=| ∠ θi-∠θi' |, i=1,2 ..., 2n
Step 5, comprehensive deformation amount is sought using weighted average method, must stretch comprehensive deformation amount is：
Reversing comprehensive deformation amount is：
WXiFor Δ BXiWeight coefficient, W ∠iFor Δ ∠iWeight coefficient, the weight coefficient is by obtained by experiment test；
Step 6, judge to stretch comprehensive deformation amount whether in stretcher strain amount threshold range, and reverse comprehensive deformation amount
Whether in torsional deflection amount threshold range, only when stretching comprehensive deformation amount and reverse comprehensive deformation amount fall into threshold range,
It is just qualified products to fly to knit vamp, is otherwise substandard product.
In an embodiment of the present invention, set location mark point is PA, and it is PF to set size-mark point, sets deformation mark
Point is P00 and P01, P10 and P11 and P20 and tri- groups of P21, then stretching distance is：
BX1=| P00-P01 |
BX2=| P10-P11 |
BX3=| P20-P21 |
BX4=| P00-PA |
BX5=| P10-PA |
BX6=| P20-PA |
BX7=| PA-P01 |
BX8=| PA-P11 |
BX9=| PA-P21 |
Windup-degree is：
∠θ1=∠ PF-PA-P00
∠θ2=∠ PF-PA-P01
∠θ3=∠ PF-PA-P10
∠θ4=∠ PF-PA-P11
∠θ5=∠ PF-PA-P20
∠θ6=∠ PF-PA-P21
The distance between size-mark point PF and position mark point PA are to obtain corresponding size, its corresponding relation such as following table institute
Show,
Table 1
Corresponding size is transferred to fly to knit the preferable stretching distance BX of vampi', i=1,2 ..., 9, preferable windup-degree ∠ θi',
I=1,2 ..., 6, stretcher strain amount threshold range Δ Xmin~Δ Xmax, torsional deflection amount threshold range Δ ∠ min~Δ ∠
max；
Calculating stretcher strain amount is：
ΔBXi=| BXi-BXi' |, i=1,2 ..., 9
Torsional deflection amount is：
Δ∠θi=| ∠ θi-∠θi' |, i=1,2 ..., 6
Δ ∠ θ i=| ∠ θ i- ∠ θ i ' |, i=1,2 ..., 6
Finally asking for stretching comprehensive deformation amount is：
Reversing comprehensive deformation amount is：
As Δ X ∈ [Δ Xmin, Δ Xmax], Δ ∠ ∈ [Δ ∠ min, Δ ∠ max], it is just qualified production to fly to knit vamp
Product, it is otherwise non-qualified products.
The present invention combines vision and known by treating to set the mark point with contrast on cut-out fly to knit vamp
The positional information of other technical limit spacing mark point, the positional information combination threshold process according to mark point obtain vamp stretcher strain amount
And torsional deflection amount, judge whether stretcher strain amount and torsional deflection amount respectively fall in stretcher strain amount threshold range and reverse and become
Whether shape amount threshold range can determine whether to fly to knit vamp qualified.For artificial detection, the detection method can realize automatic inspection
Survey, detection efficiency is high, eliminates the influence of human factor, and the stability of detection is high.
It is described above, only it is the embodiment of the present invention, is not intended to limit the scope of the present invention, thus it is every
Any subtle modifications, equivalent variations and modifications that technical spirit according to the present invention is made to above example, still fall within this
In the range of inventive technique scheme.
Claims (2)
1. a kind of the winged of view-based access control model identification knits vamp deformation detection method, it is characterised in that：By flying to knit the to be cut of vamp
Except the mark point with contrast, and the positional information of combination Visual identification technology acquisition mark point is set on part, according to mark
The positional information combination threshold process of note point obtains vamp stretcher strain amount and torsional deflection amount, judges stretcher strain amount and torsion
Whether deflection respectively falls in stretcher strain amount threshold range and torsional deflection amount threshold range can determine whether fly whether to knit vamp
It is qualified.
2. a kind of the winged of view-based access control model identification according to claim 1 knits vamp deformation detection method, it is characterised in that：Tool
Body comprises the following steps：
Step 1, fly to knit vamp treat to set the mark point with contrast on cut-out, the mark point includes position mark
Remember point, a size-mark point and at least one set of deformation mark point；
The datum mark that the position mark point identifies as vamp, it is arranged in vamp symmetrical center line and is located at whole vamp
Centre of area region；
The size-mark point is arranged in vamp symmetrical center line close to the opening position of top；
One group of deformation mark point is made up of two deformation mark points for being separately positioned on vamp both sides；
Step 2, by camera fly to knit vamp picture to flying to knit the vamp acquisition that take pictures, flying then in conjunction with Visual identification technology
Knit the position that position mark point, size-mark point and deformation mark point are obtained in vamp picture；
Step 3, open up multiple threads an information processing is marked, wherein,
First thread is used to calculate stretching distance BXi, i=1,2 ..., 3n and windup-degree ∠ θi, i=1,2 ..., 2n, n
For the group number of deformation mark point；The stretching distance is the distance between 2 deformation mark points and change in every group of deformation mark point
Shape mark point and the distance of position mark point, windup-degree are deformation mark point and the line of position mark point and the folder of center line
Angle；
Second thread is used to calculate the distance between size-mark point and position mark point to obtain corresponding size, according to the size
Transfer and fly the preferable stretching distance BX for knitting vampi', i=1,2 ..., 3n, preferable windup-degree ∠ θi', i=1,2 ..., 2n with
And stretcher strain amount threshold range Δ X min~Δ X max and torsional deflection amount threshold range Δ ∠ min~Δ ∠ max；
Step 4, stretcher strain amount and torsional deflection amount are calculated, stretcher strain amount is the stretching distance and second that first thread obtains
The absolute value of difference between the preferable stretching distance that thread obtains, i.e.,
ΔBXi=| BXi-BXi' |, i=1,2 ..., 3n
The absolute value of difference of the torsional deflection amount between the windup-degree arrived of first thread and preferable windup-degree, i.e.,
Δ∠θi=| ∠ θi-∠θi' |, i=1,2 ..., 2n
Step 5, comprehensive deformation amount is sought using weighted average method, must stretch comprehensive deformation amount is：
<mrow>
<mi>&Delta;</mi>
<mi>X</mi>
<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mrow>
<mn>3</mn>
<mi>n</mi>
</mrow>
</munderover>
<msub>
<mi>WX</mi>
<mi>i</mi>
</msub>
<mo>*</mo>
<msub>
<mi>&Delta;BX</mi>
<mi>i</mi>
</msub>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mrow>
<mn>3</mn>
<mi>n</mi>
</mrow>
</munderover>
<msub>
<mi>WX</mi>
<mi>i</mi>
</msub>
</mrow>
</mfrac>
</mrow>
Reversing comprehensive deformation amount is：
<mrow>
<mi>&Delta;</mi>
<mo>&angle;</mo>
<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>2</mn>
<mi>n</mi>
</mrow>
</munderover>
<mi>W</mi>
<mo>&angle;</mo>
<msub>
<mi>&theta;</mi>
<mi>i</mi>
</msub>
<mo>*</mo>
<mi>&Delta;</mi>
<mo>&angle;</mo>
<msub>
<mi>&theta;</mi>
<mi>i</mi>
</msub>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>2</mn>
<mi>n</mi>
</mrow>
</munderover>
<mi>W</mi>
<mo>&angle;</mo>
<msub>
<mi>&theta;</mi>
<mi>i</mi>
</msub>
</mrow>
</mfrac>
</mrow>
WXiFor Δ BXiWeight coefficient, W ∠iFor Δ ∠iWeight coefficient；
Step 6, judge to stretch comprehensive deformation amount whether in stretcher strain amount threshold range, and whether reverse comprehensive deformation amount
In torsional deflection amount threshold range, only when stretching comprehensive deformation amount and reverse comprehensive deformation amount fall into threshold range, fly to knit
Vamp is just qualified products, is otherwise substandard product.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710502157.8A CN107356207B (en) | 2017-06-27 | 2017-06-27 | A kind of the winged of view-based access control model identification knits vamp deformation detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710502157.8A CN107356207B (en) | 2017-06-27 | 2017-06-27 | A kind of the winged of view-based access control model identification knits vamp deformation detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107356207A true CN107356207A (en) | 2017-11-17 |
| CN107356207B CN107356207B (en) | 2019-04-16 |
Family
ID=60273711
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710502157.8A Active CN107356207B (en) | 2017-06-27 | 2017-06-27 | A kind of the winged of view-based access control model identification knits vamp deformation detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107356207B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108346161A (en) * | 2017-12-18 | 2018-07-31 | 上海咔咻智能科技有限公司 | Winged based on image knits vamp matching locating method and its system and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1784595A (en) * | 2003-03-27 | 2006-06-07 | 马洛有限及两合公司 | Method for inspecting the quality criteria of flat textile structures embodied in a multilayer form according to a contour |
| CN101979751A (en) * | 2010-09-28 | 2011-02-23 | 中华人民共和国陕西出入境检验检疫局 | Method for detecting dimensional stability of fabric based on image analysis |
| CN106767484A (en) * | 2017-02-13 | 2017-05-31 | 北京和众视野科技有限公司 | Fabric size rate of change automatic testing method, apparatus and system |
-
2017
- 2017-06-27 CN CN201710502157.8A patent/CN107356207B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1784595A (en) * | 2003-03-27 | 2006-06-07 | 马洛有限及两合公司 | Method for inspecting the quality criteria of flat textile structures embodied in a multilayer form according to a contour |
| CN101979751A (en) * | 2010-09-28 | 2011-02-23 | 中华人民共和国陕西出入境检验检疫局 | Method for detecting dimensional stability of fabric based on image analysis |
| CN106767484A (en) * | 2017-02-13 | 2017-05-31 | 北京和众视野科技有限公司 | Fabric size rate of change automatic testing method, apparatus and system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108346161A (en) * | 2017-12-18 | 2018-07-31 | 上海咔咻智能科技有限公司 | Winged based on image knits vamp matching locating method and its system and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107356207B (en) | 2019-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104268591B (en) | A kind of facial critical point detection method and device | |
| CN103604809B (en) | A kind of online visible detection method of pattern cloth flaw | |
| CN106485275B (en) | A method of realizing that cover-plate glass is bonded with liquid crystal display positioning | |
| CN105701820B (en) | A kind of point cloud registration method based on matching area | |
| CN105094337B (en) | A kind of three-dimensional gaze estimation method based on iris and pupil | |
| CN108269269A (en) | Method for tracking target and device | |
| CN109344882A (en) | Robot based on convolutional neural networks controls object pose recognition methods | |
| CN106529409A (en) | Eye ocular fixation visual angle measuring method based on head posture | |
| CN104637041B (en) | A kind of broad fabrics image based on reference feature is obtained and joining method | |
| US11205262B2 (en) | Online detection method of circular weft knitting stripe defects based on gray gradient method | |
| CN109977781A (en) | Method for detecting human face and device, readable storage medium storing program for executing | |
| CN110008857A (en) | A kind of human action matching methods of marking based on artis | |
| CN109261528A (en) | Express delivery sorting method and device based on binocular vision | |
| CN108346161B (en) | Flying woven vamp matching and positioning method based on image, system and storage medium thereof | |
| CN107966444A (en) | Textile flaw detection method based on template | |
| CN107356207A (en) | A kind of the winged of view-based access control model identification knits vamp deformation detection method | |
| CN106157264A (en) | Large area image uneven illumination bearing calibration based on empirical mode decomposition | |
| CN104598914A (en) | Skin color detecting method and device | |
| CN103136513B (en) | A kind of ASM man face characteristic point positioning method of improvement | |
| Zhang et al. | Weave pattern recognition by measuring fiber orientation with Fourier transform | |
| CN107944393A (en) | Face nose localization method | |
| CN107765463A (en) | A kind of detection method of instrument board liquid crystal display | |
| CN109002745A (en) | A kind of lane line real-time detection method based on deep learning and tracking technique | |
| CN108898573A (en) | Infrared small target rapid extracting method based on multi-direction annular gradient method | |
| TW202307305A (en) | System and method for determining joinder locations for assembly of garments |
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 | ||
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20171117 Assignee: QUANZHOU HUASHU ROBOT CO.,LTD. Assignor: QUANZHOU-HUST INTELLIGENT MANUFACTURING FUTURE Contract record no.: X2023350000282 Denomination of invention: A Visual Recognition Based Deformation Detection Method for Flywoven Upper Granted publication date: 20190416 License type: Common License Record date: 20230606 |