CN207238542U - A kind of thin bamboo strip defect on-line detecting system based on machine vision - Google Patents
A kind of thin bamboo strip defect on-line detecting system based on machine vision Download PDFInfo
- Publication number
- CN207238542U CN207238542U CN201720990596.3U CN201720990596U CN207238542U CN 207238542 U CN207238542 U CN 207238542U CN 201720990596 U CN201720990596 U CN 201720990596U CN 207238542 U CN207238542 U CN 207238542U
- Authority
- CN
- China
- Prior art keywords
- conveyer belt
- thin bamboo
- bamboo strip
- air knife
- formula conveyer
- 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.)
- Expired - Fee Related
Links
Landscapes
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The utility model discloses a kind of thin bamboo strip defect on-line detecting system based on machine vision.Including Industrial PC Computer, PLC controller, air pump, two solenoid valves, two air knives, isolation board, line-scan digital camera, area array cameras, two motors, two narrow formula conveyer belt, four photoelectric sensors.Conveyer belt send thin bamboo strip to area array cameras and line-scan digital camera, gathers the direct picture and verso images of thin bamboo strip, and input picture to Industrial PC Computer carries out image preprocessing；Profile, surface defects detection are carried out again；Draw profile defects, surface defect, flawless product three classes, classification results are exported to PLC controller by serial ports, opened by PLC controller control solenoid valve, rejected with air knife；If zero defect, PLC controller is failure to actuate.The utility model avoids missing inspection flase drop problem existing for artificial detection so that Detection accuracy improves；Using database purchase historical data, convenient inquiry, it is accurate to count.The system is clear in structure, easy to operate.
Description
Technical field
Frame processing system is the utility model is related to, is existed more particularly, to a kind of thin bamboo strip defect based on machine vision
Line detecting system.
Background technology
Thin bamboo strip is the thin slice being split into bamboo, and that is applied on the bamboo handicraft articles such as fan, summer sleeping mat is more.First slight machine by broken
Bamboo chip is divided into relatively thin thin bamboo strip, such as Chinese patent bulletin CN 105835144A（Date of declaration：On August 10th, 2016）It is open
A kind of efficiently bamboo, which is broken, slights machine.Due to bamboo kind, the reason for quality or resting period, there are it is certain the defects of, such as profile
Defect, surface texture defect etc..The product of usual profile defects is waste product；The product of surface texture defect is referred to as substandard products, price
It is relatively low；Lines it is neat for certified products, higher price.Producer is also being sorted and is being counted with the method for traditional artificial detection at present
Number.The method detection efficiency is low, often since excessive missing inspection and flase drop, high labor cost occurs in the fatigue of people.Take one
The effective method of kind detects it is particularly important that thin bamboo strip is for whole bamboo product industry.
Utility model content
Deficiency in view of the aforementioned technical background, the purpose of this utility model is to provide a kind of thin bamboo strip based on machine vision
Defect on-line detecting system, the equipment combination vision collecting system such as conveyer belt, Industrial PC, PLC device is realized to thin bamboo strip quality
Real-time monitoring classification, to improve the intelligence degree of bamboo product industry industrial automation, accelerate the work efficiency of factory's entirety,
Save the time cost of artificial treatment.
In order to achieve the above object, the utility model is using technical solution：
The utility model includes Industrial PC Computer, PLC controller, air pump, two solenoid valves, two air knives, isolation board, linear array
Camera, area array cameras, two motors, two narrow formula conveyer belt, four photoelectric sensors.
Two narrow formula conveyer belt is arranged in " linear type ", and gap is left between two narrow formula conveyer belt, is equipped with below gap
The line-scan digital camera of thin bamboo strip verso images is gathered, line-scan digital camera is installed on annular light source center, and first narrow formula conveyer belt is by first
Motor-driven rotation, the narrow formula conveyer belt of Article 2 is by the second motor-driven rotation；It is equipped with and adopts above first narrow formula conveyer belt
Collect the area array cameras of thin bamboo strip direct picture, area array cameras is installed on another annular light source center, line-scan digital camera and area array cameras point
Industrial PC Computer is not connect；First photoelectric sensor equipped with detection thin bamboo strip end on the left of area array cameras, gap top is equipped with detection
Second photoelectric sensor of thin bamboo strip front end, the side of the narrow formula conveyer belt of Article 2 are provided with the first wind in " linear type " arrangement
Knife and the second air knife, the 3rd photoelectric sensor and the 4th photoelectric sensor are respectively positioned on the top of the narrow formula conveyer belt of Article 2, close
The 3rd photoelectric sensor equipped with detection thin bamboo strip end on the left of first air knife of first article narrow formula conveyer belt side, the second air knife are left
Four photoelectric sensor of the side equipped with detection thin bamboo strip end, the first air knife are connected with the first solenoid valve and PLC controller, and second
Air knife is connected with second solenoid valve and PLC controller, and two solenoid valves are connected with air pump outlet；The narrow formula conveyer belt of Article 2 it is another
Side is equipped with isolation board, and for isolation board between the first air knife and the second air knife, four photoelectric sensors connect PLC controls respectively
Device, PLC controller connection Industrial PC Computer.
Gap is left between the two narrow formula conveyer belt, its gap is 4cm~6cm.
It is described to be located in first air knife and the second air knife air outlet of " linear type " arrangement with thin bamboo strip in same level.
The isolation board being located between the first air knife and the second air knife, vertically with the narrow formula conveyer belt side of Article 2, and
Isolation board is higher than the narrow formula conveyer belt plane of Article 2.
The utility model has an advantageous effect in that：
1) machine vision technique is utilized, avoids missing inspection flase drop problem existing for artificial detection so that Detection accuracy carries
Height, saves human cost and time cost.
2) database purchase historical data is utilized, convenient inquiry, it is accurate to count.
3) the system is clear in structure, easy to operate, accurate by the use of air knife as sorting equipment, sorting.
Brief description of the drawings
Fig. 1 is the structure diagram of the utility model.
Fig. 2 is the workflow schematic diagram of the utility model.
In Fig. 1：1st, Industrial PC Computer, 2, PLC controller, 3, air pump, the 4, second motor, the 5, second air knife, 6, isolation board, 7,
Line-scan digital camera, 8, area array cameras, the 9, first motor, 10, the narrow formula conveyer belt of first segment, the 11, first photoelectric sensor, 12, second
Photoelectric sensor, the 13, the 3rd photoelectric sensor, the 14, first air knife, the 15, the 3rd photoelectric sensor, the 16, first solenoid valve, 17,
Second solenoid valve, 18, the narrow formula conveyer belt of second segment.
Embodiment
It is further described with reference to the accompanying drawings and examples and to the utility model.
As shown in Figure 1, the utility model includes Industrial PC Computer 1, and PLC controller 2, air pump 3, two solenoid valves 16,17, two
A air knife 14,5, isolation board 6, line-scan digital camera 7, area array cameras 8, two motors 9,4, two narrow formula conveyer belt 10,18, four light
Electric transducer 11,12,13,15；
Two narrow formula conveyer belt 10,18 is arranged in " linear type ", and gap is left between two narrow formula conveyer belt 10,18,
Line-scan digital camera 7 equipped with collection thin bamboo strip verso images below gap, line-scan digital camera 7 are installed on annular light source center, first narrow formula
Conveyer belt 10 drives rotation by the first motor 9, and the narrow formula conveyer belt 18 of Article 2 drives rotation by the second motor 4；It is narrow at first
Area array cameras 8 of the top of formula conveyer belt 10 equipped with collection thin bamboo strip direct picture, area array cameras 8 are installed in another annular light source
The heart, line-scan digital camera 7 and area array cameras 8 connect Industrial PC Computer 1 respectively；The left side of area array cameras 8 is equipped with first of detection thin bamboo strip end
Photoelectric sensor 11, second photoelectric sensor 12 of the gap top equipped with detection thin bamboo strip front end, the narrow formula conveyer belt 18 of Article 2
Side be provided with " linear type " arrangement the first air knife 14 and the second air knife 5, the 3rd photoelectric sensor 13 and the 4th photoelectricity
Sensor 15 is respectively positioned on the top of the narrow formula conveyer belt 18 of Article 2, the first air knife 14 close to first narrow 10 side of formula conveyer belt
Threeth photoelectric sensor 13 of the left side equipped with detection thin bamboo strip end, the left side of the second air knife 5 are equipped with the 4th of detection thin bamboo strip end
Photoelectric sensor 15, the first air knife 14 are connected with the first solenoid valve 16 and PLC controller 2, the second air knife 5 and second solenoid valve 17
Connected with PLC controller 2, two solenoid valves 16,17 are connected with the outlet of air pump 3；The opposite side dress of the narrow formula conveyer belt 18 of Article 2
There is isolation board 6, isolation board 6 between the first air knife 14 and the second air knife 5, distinguish by four photoelectric sensors 11,12,13,15
PLC controller 2 is connect, PLC controller 2 connects Industrial PC Computer 1.
Gap is left between the two narrow formula conveyer belt 10,18, its gap is 4cm~6cm.
It is described to be located at same level with thin bamboo strip in first air knife 14 and 5 air outlet of the second air knife of " linear type " arrangement
On.
The isolation board 6 being located between the first air knife 14 and the second air knife 5, vertically with narrow 18 side of formula conveyer belt of Article 2
Face, and isolation board 6 is higher than narrow 18 plane of formula conveyer belt of Article 2.
The area array cameras 8, line-scan digital camera 7, the first air knife 14, the second air knife 5 and appliance component can be commercially
Choose.
As shown in Fig. 2, the step of detection method of the utility model, is：
Step I) with area array cameras collection thin bamboo strip direct picture, with line-scan digital camera collection thin bamboo strip verso images, input
Image to Industrial PC Computer 1 carries out image preprocessing；
Step II) pass through the image progress profile defects detection after locating in advance and surface defects detection；
Step III) passes through step II) draw three kinds of testing results, it is that profile defects (waste product), surface defect are (secondary respectively
Product) and zero defect (certified products) product three classes, classification results are exported to PLC controller by serial ports, if it is detected that profile has
Defect, is opened by the first solenoid valve of PLC controller control, is rejected with first air knife；If there is one side in two sides and have above
Surface defect, then opened by the second solenoid valve of PLC controller control, rejected with second air knife；If zero defect, PLC controls
Device is failure to actuate, and no defective product is fallen into specified region along assembly line by conveyer belt and is stored.
The step I) in image preprocessing, Industrial PC Computer is received into the image that vision collecting device sends and is located in advance
Reason, including following steps：
Step 1) image gray processing, i.e., be changed into the coloured image of RGB Three-channel datas the gray-scale map of single-channel data
Picture, obtains gray level image, specific formula (1) is as follows according to weighted average method：
f(i,j)=0.30R(i,j)+0.59G(i,j)+0.11B(i,j) (1) ；
In formula (1), i represents the abscissa of image array, and j represents the ordinate of image array, and (i, j) is represented in image
All points, R (i, j) represent glow angle value, and G (i, j) represents green brightness value, and B (i, j) represents blue brightness value；
Step 2) image enhancement, i.e., convert data to original image by image processing algorithm, protrude contour feature in image
Or suppress unwanted textural characteristics in image, image is matched with eye response characteristic, using spatial domain method in image
Pixel carry out operation reach ideal effect, specific formula (2) is as follows：
g(x,y)=f(x,y)*h(x,y) (2) ；
In formula (2), f (x, y) is original image；H (x, y) is space transfer function；G (x, y) is represented after being handled
Image；
Step 3) image filtering, that is, remove the noise in target and background, at the same protect the shape of image object, size and
Specific geometry and topological features, are handled image using Gaussian smoothing filter；
Step 4) image binaryzation, i.e., chosen the gray level image of 256 brightness degrees by threshold value and remained to instead to obtain
Reflect that image is overall and the binary image of local feature, binary map is obtained using based on the adaptive threshold fuzziness of histogram
Picture.
The step II) in profile defects detect, including following steps：
Step 1) gathers training sample
Several qualified thin bamboo strip contour images are gathered, it is any to choose width training figure for training differently contoured comparison model
Picture, extraction qualification slight piece profile as contour mould, prepare for next search profile and coarse sizing；
Step 2) training profile comparison model
The training image collected is handled successively, is concretely comprised the following steps：
A) to searching for qualified thin bamboo strip profile in pretreated training image, obtain qualification in training image slight piece profile and
Centre coordinate；
B) training image is registered to by reference point by coordinate transform, and the training image after alignment is stored in profile ratio
Compared with template；
C) all training images are trained successively, and calculate the average gray and standard deviation of each point in profile comparison model,
Using gray value standard difference as the index of checking contour comparison model quality, and it regard each point average gray value after screening as this
Point gray value is stored as profile comparison model the average image as difference shadow method benchmark image；
Step 3) difference shadow method detects
Be loaded into it is to be detected slight piece contour images, contour mould is searched in image to be detected, is set when carrying out template matches
Threshold value is put, profile defects are significantly slightd with piece and carries out coarse sizing, profile defects are slightd into piece and are directly rejected, after coarse sizing
Slight piece and enter difference shadow method detection, the piece of slighing after coarse sizing is aligned by coordinate transform with reference point, then compared with profile
The average image in model carries out difference shadow method detection, sets threshold value, and gray scale difference is more than the point of threshold value as defect point.
The step II) in surface defects detection, including following steps：
Step 1) feature extraction
Principal component analysis dimensionality reduction is carried out with the feature of defect to non-defective, eigenmatrix Principal component is extracted, to dimensionality reduction knot
Fruit carries out cluster analysis, obtains the distance between all kinds of defects；
Step 2) designs DAGSVM structures
The node as decision tree from top to bottom is arranged successively according to the size descending of similarity degree between all kinds of defects, is utilized
A large amount of template combination nominal data training DAGSVM, and decision tree structure and parameter are optimized；
Step 3) defect is classified
Defect characteristic is obtained by feature extraction to thin bamboo strip to be detected, by the DAGSVM of design to the feature extracted
Classify.
The camera of the utility model is fixedly installed on camera support, detects to obtain thin bamboo strip positioned at bat by photoelectric sensor
According to position, Industrial PC Computer sends photographing instruction, and sends the image obtained to industry by image capture interface module
PC.After being handled by Industrial PC Computer, result is output to PLC controller, the solenoid valve that PLC controller controls is made accordingly
Action, thin bamboo strip is sorted by high-speed flow.The data that PLC controller detects sensor for countering, which are passed in database, protects
Deposit.
Industrial PC Computer has the function of image analysis processing and control PLC controller and performs following steps：
1st, the signal of photoelectric sensor is received, controls light source and camera, gathers picture.
2nd, image is pre-processed, specifically included：Image gray processing, image enhancement, image filtering, image binaryzation.
3rd, for piece profile defects are slightd, profile comparison model is established.Slight picture using a large amount of qualifications and pass through template
Profile comparison model is trained after carrying out spacial alignment with technology, qualification is obtained and slights piece the average image, passes through difference shadow method and detects alignment
Afterwards to be detected slights picture.
4th, for slighing piece surface defect.Construct DAGSVM structures.Non-defective and two class defects are extracted first（Spot and
Colo(u)r breakup）Feature, and to eigenmatrix carry out principal component analysis, to defect characteristic dimensionality reduction；Secondly dimensionality reduction result is carried out
Hierarchial cluster analysis, decision tree is built according to the distance between all kinds of defects；Finally by training sample to support vector machines
Optimize, build each node supporting vector machine model of decision tree.
5th, the result of classification is changed into logic control value, exports to PLC controller, act PLC controller, complete to divide
Pick task.
6th, receive the enumeration data of three kinds of products of PLC controller upload, and be stored in the database in Industrial PC Computer.
Claims (4)
- A kind of 1. thin bamboo strip defect on-line detecting system based on machine vision, it is characterised in that：Including Industrial PC Computer (1), PLC controls Device (2) processed, air pump (3), two solenoid valves (16,17), two air knives (14,5), isolation board (6), line-scan digital camera (7), face battle array phase Machine (8), two motors (9,4), two narrow formula conveyer belt (10,18), four photoelectric sensors (11,12,13,15)；Two narrow formula conveyer belt (10,18) arranges in " linear type ", and gap is left between two narrow formula conveyer belt (10,18), Line-scan digital camera (7) equipped with collection thin bamboo strip verso images below gap, line-scan digital camera (7) are installed on annular light source center, first Narrow formula conveyer belt (10) is driven by the first motor (9) and rotated, and the narrow formula conveyer belt (18) of Article 2 is turned by the second motor (4) driving It is dynamic；Area array cameras (8) equipped with collection thin bamboo strip direct picture above first narrow formula conveyer belt (10), area array cameras (8) peace Mounted in another annular light source center, line-scan digital camera (7) and area array cameras (8) connect Industrial PC Computer (1) respectively；Area array cameras (8) is left First photoelectric sensor (11) of the side equipped with detection thin bamboo strip end, second photoelectricity of the gap top equipped with detection thin bamboo strip front end Sensor (12), the side of the narrow formula conveyer belt (18) of Article 2 are provided with the first air knife (14) and second in " linear type " arrangement Air knife (5), the 3rd photoelectric sensor (13) and the 4th photoelectric sensor (15) are respectively positioned on the upper of the narrow formula conveyer belt (18) of Article 2 Side, the 3rd photoelectric transfer equipped with detection thin bamboo strip end on the left of the first air knife (14) of first article narrow formula conveyer belt (10) side Sensor (13), the 4th photoelectric sensor (15) equipped with detection thin bamboo strip end on the left of the second air knife (5), the first air knife (14) with First solenoid valve (16) and PLC controller (2) connection, the second air knife (5) connect with second solenoid valve (17) and PLC controller (2) Connect, two solenoid valves (16,17) connect with air pump (3) outlet；The opposite side of the narrow formula conveyer belt (18) of Article 2 is equipped with isolation board (6), isolation board (6) is between the first air knife (14) and the second air knife (5), four photoelectric sensors (11,12,13,15) point PLC controller (2), PLC controller (2) connection Industrial PC Computer (1) are not connect.
- A kind of 2. thin bamboo strip defect on-line detecting system based on machine vision according to claim 1, it is characterised in that：Institute State and leave gap between two narrow formula conveyer belt (10,18), its gap is 4cm~6cm.
- A kind of 3. thin bamboo strip defect on-line detecting system based on machine vision according to claim 1, it is characterised in that：Institute State and be located in first air knife (14) and the second air knife (5) air outlet of " linear type " arrangement with thin bamboo strip in same level.
- A kind of 4. thin bamboo strip defect on-line detecting system based on machine vision according to claim 1, it is characterised in that：Institute Isolation board (6) of the rheme between the first air knife (14) and the second air knife (5), vertically formula conveyer belt (18) side narrow with Article 2 Face, and isolation board (6) is higher than narrow formula conveyer belt (18) plane of Article 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201720990596.3U CN207238542U (en) | 2017-08-09 | 2017-08-09 | A kind of thin bamboo strip defect on-line detecting system based on machine vision |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201720990596.3U CN207238542U (en) | 2017-08-09 | 2017-08-09 | A kind of thin bamboo strip defect on-line detecting system based on machine vision |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN207238542U true CN207238542U (en) | 2018-04-17 |
Family
ID=61879720
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201720990596.3U Expired - Fee Related CN207238542U (en) | 2017-08-09 | 2017-08-09 | A kind of thin bamboo strip defect on-line detecting system based on machine vision |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN207238542U (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107486415A (en) * | 2017-08-09 | 2017-12-19 | 中国计量大学 | Thin bamboo strip defect on-line detecting system and detection method based on machine vision |
| CN111229648A (en) * | 2020-01-19 | 2020-06-05 | 青岛滨海学院 | Solar cell panel flaw detection system and detection method based on machine vision |
| CN112329531A (en) * | 2020-09-30 | 2021-02-05 | 山东大学 | Linear array binocular imaging system for pipe gallery apparent disease detection and working method |
| CN113252561A (en) * | 2021-05-21 | 2021-08-13 | 浙江科技学院 | Cookware surface defect detection system and method |
| CN114474231A (en) * | 2021-12-31 | 2022-05-13 | 赣州市南康区城发家具产业智能制造有限责任公司 | Hierarchical production line is cut to wood piece intelligence of planing light |
| CN114527075A (en) * | 2022-02-28 | 2022-05-24 | 中科芯集成电路有限公司 | Mask defect detection device and method |
-
2017
- 2017-08-09 CN CN201720990596.3U patent/CN207238542U/en not_active Expired - Fee Related
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107486415A (en) * | 2017-08-09 | 2017-12-19 | 中国计量大学 | Thin bamboo strip defect on-line detecting system and detection method based on machine vision |
| CN107486415B (en) * | 2017-08-09 | 2023-10-03 | 中国计量大学 | Thin bamboo strip defect online detection system and detection method based on machine vision |
| CN111229648A (en) * | 2020-01-19 | 2020-06-05 | 青岛滨海学院 | Solar cell panel flaw detection system and detection method based on machine vision |
| CN112329531A (en) * | 2020-09-30 | 2021-02-05 | 山东大学 | Linear array binocular imaging system for pipe gallery apparent disease detection and working method |
| CN113252561A (en) * | 2021-05-21 | 2021-08-13 | 浙江科技学院 | Cookware surface defect detection system and method |
| CN114474231A (en) * | 2021-12-31 | 2022-05-13 | 赣州市南康区城发家具产业智能制造有限责任公司 | Hierarchical production line is cut to wood piece intelligence of planing light |
| CN114474231B (en) * | 2021-12-31 | 2023-01-24 | 赣州市南康区城发家具产业智能制造有限责任公司 | Hierarchical production line is cut to wood piece intelligence of planing light |
| CN114527075A (en) * | 2022-02-28 | 2022-05-24 | 中科芯集成电路有限公司 | Mask defect detection device and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107486415A (en) | Thin bamboo strip defect on-line detecting system and detection method based on machine vision | |
| CN207238542U (en) | A kind of thin bamboo strip defect on-line detecting system based on machine vision | |
| CN105891233B (en) | Lens surface defect intelligent checking system and its implementation based on machine vision | |
| CN105388162B (en) | Raw material silicon chip surface scratch detection method based on machine vision | |
| CN101234381B (en) | Granular material sorting classifying method based on visual sense recognition | |
| Rokunuzzaman et al. | Development of a low cost machine vision system for sorting of tomatoes. | |
| CN102998316B (en) | Transparent liquid impurity detection system and detection method thereof | |
| CN102654464A (en) | Copper strip surface defect detection system based on multi-feature fuzzy recognition | |
| Deng et al. | A carrot sorting system using machine vision technique | |
| CN108318494B (en) | The red online vision detection and classification devices and methods therefor for proposing fruit powder | |
| CN109615635A (en) | The method and device of quality sorting is carried out to strawberry based on image recognition | |
| CN111563519B (en) | Tea impurity identification method and sorting equipment based on Stacking weighting integrated learning | |
| CN104952754B (en) | Silicon chip method for separating after plated film based on machine vision | |
| CN106248680A (en) | A kind of engine commutator quality detecting system based on machine vision and detection method | |
| CN103308525A (en) | Online detection method and device for metal wire production | |
| CN113569922A (en) | Intelligent lossless apple sorting method | |
| Sidehabi et al. | The Development of Machine Vision System for Sorting Passion Fruit using Multi-Class Support Vector Machine. | |
| CN102680488B (en) | Device and method for identifying massive agricultural product on line on basis of PCA (Principal Component Analysis) | |
| Zhang et al. | Fabric defect detection based on visual saliency map and SVM | |
| CN113145473A (en) | Intelligent fruit sorting system and method | |
| Huang et al. | Mango surface defect detection based on HALCON | |
| CN112179846A (en) | Prefabricated convex window defect detection system based on improved Faster R-CNN | |
| CN205581025U (en) | Hierarchical detecting system of finish plane bamboo strip vision letter sorting | |
| CN202614694U (en) | Recognition device for digital edible mushroom impurities | |
| CN111359892A (en) | Device and method for detecting and grading appearance of Tianbao honey pomelos based on machine vision |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180417 Termination date: 20210809 |