CN110335239A - Defects detection training machine and its application method based on deep learning - Google Patents

Defects detection training machine and its application method based on deep learning Download PDF

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Publication number
CN110335239A
CN110335239A CN201910385380.8A CN201910385380A CN110335239A CN 110335239 A CN110335239 A CN 110335239A CN 201910385380 A CN201910385380 A CN 201910385380A CN 110335239 A CN110335239 A CN 110335239A
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CN
China
Prior art keywords
light source
deep learning
module
camera lens
personal computer
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Pending
Application number
CN201910385380.8A
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Chinese (zh)
Inventor
胡江洪
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Fitow Tianjin Detection Technology Co ltd
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Feite Tianjin Intelligent Technology Co ltd
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Priority to CN201910385380.8A priority Critical patent/CN110335239A/en
Publication of CN110335239A publication Critical patent/CN110335239A/en
Pending legal-status Critical Current

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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B07SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07CPOSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34Sorting according to other particular properties
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B07SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07CPOSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/36Sorting apparatus characterised by the means used for distribution
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N21/88Investigating the presence of flaws or contamination
    • G01N21/8851Scan 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for recognising patterns
    • G06K9/62Methods or arrangements for pattern recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • G06K9/628Multiple classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0639Performance analysis
    • G06Q10/06395Quality analysis or management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0004Industrial image inspection
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N21/88Investigating the presence of flaws or contamination
    • G01N21/8851Scan 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
    • G01N2021/8887Scan 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20081Training; Learning
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30108Industrial image inspection
    • G06T2207/30164Workpiece; Machine component

Abstract

The invention discloses a kind of defects detection training machine and its application method based on deep learning, bogie, side light source, programmable light source and camera lens assembly are adjusted including industrial personal computer, bottom light source, turntable, distance, programmable light source and camera lens assembly adjust bogie with distance respectively and connect, and the lower end camera lens of camera lens assembly downward and is directed at the upper end outlet of programmable light source.Application method includes the following steps: light emitting region and brightness that programmable light source 1) is adjusted by the equipment management system of industrial personal computer, to obtain image data;2) image data of acquisition is subjected to algorithm training by the deep learning software of industrial personal computer, and the algorithm model that training generates detects workpiece for measurement;3) classification and statistics of defects count, defect classification are carried out to workpiece for measurement, and qualified product and non-qualified product are grabbed by manipulator.The a variety of environment of analog of the present invention, fast implement trained detection, improve the efficiency and accuracy of detection.

Description

Defects detection training machine and its application method based on deep learning
Technical field
The invention belongs to industry spot defect detecting technique field, in particular to a kind of defects detection based on deep learning Training machine and its application method.
Background technique
In industrial scene, there is very strict requirement to the factory index of product parts, these requirements are product zero The effect of functioning property provides guarantee after part factory.Under industrial scene, various machinery, sound, light, the complex environment of electricity and crowd Multi-process may be damaged to the appearance of product parts, become the product parts with defect.It is largely directed at present The defects detection of product parts is all highly dependent on manually to be detected one by one, occupies a large amount of human resources, is consumed a large amount of Human cost.Least a portion of defects detection is to be solved by traditional image processing algorithm, but have the defects that such as to detect kind The deficiencies of class is very single, and the early period of Processing Algorithm, verifying was extremely complex, and then influence the efficiency of detection.
Summary of the invention
For the technical problems in the prior art, the present invention provides a kind of defects detection training based on deep learning Machine and its application method.
In order to solve the above technical problems, the technical solution adopted by the present invention is that:
A kind of defects detection training machine based on deep learning, including industrial personal computer, bottom light source, turntable, distance are adjusted Bogie, side light source, programmable light source and camera lens assembly are equipped with deep learning software and equipment pipe in the industrial personal computer Reason system, the bottom light source are located at the lower section of turntable, and the distance adjusts rotating turret and is located above the turntable, the programmable Light source is connect by light source shelf with apart from adjusting bogie, and the camera lens assembly is connect with apart from adjusting bogie, and institute It states the lower end camera lens of camera lens assembly downward and is directed at the upper end outlet of programmable light source.
Preferably, it is linear mould group that the distance, which adjusts bogie,.
Preferably, the bottom light source and side light source are luminescent panel.
A kind of application method of the defects detection training machine based on deep learning, includes the following steps:
1) light emitting region and the brightness of programmable light source are adjusted, by the equipment management system of industrial personal computer to obtain picture number According to;
2) image data that will acquire carries out algorithm training by the deep learning software of industrial personal computer, and training is generated Algorithm model detects workpiece for measurement;
3) classification and statistics of defects count, defect classification are carried out to workpiece for measurement, and qualified product is grabbed by manipulator With non-qualified product.
5. the application method of the defects detection training machine according to claim 4 based on deep learning, feature It is, the deep learning software includes:
Labeling module: for realizing the labeling of the image data to camera and camera lens acquisition, and defect image is carried out Type mark, is the data source of post deep learning training;
Training module: learning the data marked, to obtain optimal disaggregated model;
Cloud storage module: the image at scene is returned in Cloud Server, the data-optimized of later period is carried out;
Test module: trained model is loaded into, and the image acquired by camera and lens assembly is lacked in real time Fall into detection.
Preferably, the equipment management system comprises the following modules:
Light source camera control module: the periodic light on and off of light source, stroboscopic are realized by software or hardware trigger interface.
Machine control modules: for controlling angle direction, the distance of light source to realize automatic movement;
Scheme memory module: it is used for the different types of polishing mode of record storage;
External communication module: there is the interface externally communicated with PLC and manipulator and the integral machine of entire combination;
The automatic categorization module of product: for variety classes product classification;
Product quality warning module: it is used for real-time statistics defects count;
Real-time online rejects module: for rejecting faulty goods/mixing by the device for eliminating outside docking
Compared with prior art, the present invention has the beneficial effects that the present invention utilizes adjustable programmable light source And equipment management system, there can be a variety of environment of simulation, and fast implement trained detection, improve the efficiency and accuracy of detection.
Detailed description of the invention
Fig. 1 is the schematic view of the front view of the defects detection training machine based on deep learning in the present invention;
Fig. 2 is the side structure schematic view of the defects detection training machine based on deep learning in the present invention;
Fig. 3 is the sectional structure signal of the programmable light source of the defects detection training machine based on deep learning in the present invention Figure;
Fig. 4 is that the programmable light source of defects detection training machine based on deep learning in the present invention looks up structural representation Figure;
Fig. 5 is the test specimen to be measured of the defects detection training machine based on deep learning and the relationship of bottom light source in the present invention Schematic diagram;
Fig. 6 is the flow diagram of the defect application method based on deep learning in the present invention.
In figure, 1. bottom light sources;2. turntable;3. distance adjusts bogie;4. side light source;5. programmable light source;51. bowl-shape Body;52. light source up;Qu Guangyuan in 53.;54. lower area's light source;6. camera lens assembly;7. workpiece for measurement;8. light source shelf;9. Screw.
Specific embodiment
Technical solution in order to enable those skilled in the art to better understand the present invention, in the following with reference to the drawings and specific embodiments It elaborates to the present invention.
As shown in Figures 1 to 6, embodiment of the invention discloses a kind of defects detection training airplane based on deep learning Device, including industrial personal computer (not shown), bottom light source 1, turntable 2, distance adjust bogie 3, side light source 4, programmable light source 5 With camera lens assembly 6, bottom light source 1 and side light source 4 are luminescent panel.Deep learning software is installed in industrial personal computer and is set Standby management system bottom light source 1 is located at the lower section of turntable 2, and distance adjusts the top that bogie 3 is located at turntable 2, and programmable light Source 5 is connect by light source shelf 8 with apart from adjusting bogie 3, and camera lens assembly 6 is connect with apart from adjusting bogie 3, and camera The lower end camera lens of lens assembly 6 downward and is directed at the upper end outlet of programmable light source 5.
In the present embodiment, it is linear mould group that distance, which adjusts bogie 3,.
In the present embodiment, the rotating manner of turntable 2 is the prior art, such as can be motor and drive turntable by gear pair Rotation etc..
Programmable light source is connect by light source shelf 8 and screw 9 with apart from adjusting bogie 3, can journey by process control The circumferential direction of formula light source is divided into eight sections, and is axially divided into area's light source 51, middle area's light source 52 and lower area light source 53, and logical The brightness in each section and upper area's light source 51, middle area's light source 52 and lower area light source 53 can individually be controlled by crossing program.
The application method of the invention also discloses a kind of defects detection training machine based on deep learning, including walk as follows It is rapid:
1) light emitting region and the brightness of programmable light source 5 are adjusted by the equipment management system of industrial personal computer;
2) algorithm training is carried out by the deep learning software of industrial personal computer, and the algorithm model that training is generated is to work to be measured Part is detected;
3) image data of acquisition is uploaded in the Cloud Server for the deep learning software being pre-installed, and passes through equipment pipe Reason system to workpiece for measurement 10 carry out defects count, defect classification classification and statistics, and by manipulator grab qualified product with Non- qualified product.
Acquire the photo of workpiece for measurement (workpiece for measurement is placed on turntable 2) by camera and lens assembly 6, and by acquisition Photographic intelligence uploads to the Cloud Server in deep learning software, for improving detection model.
Loading and unloading mechanical device is accessed by equipment management system, part is grabbed by the mechanical arm of loading and unloading mechanical device To detection platform, by detection platform output as a result, and grabbing rejected product to not by the mechanical arm of loading and unloading mechanical device Qualified product area, qualified product are grabbed to qualified product area.
In the present invention, deep learning software includes:
Labeling module: for realizing the labeling of the image data to camera and camera lens acquisition, and defect image is carried out Type mark is the data source of deep learning later period training;
Training module: learning the data marked, to obtain optimal disaggregated model;
Cloud storage module: the image at scene is returned in Cloud Server, the data-optimized of later period is carried out;
Test module: trained model is loaded into, and the image acquired by camera and lens assembly is lacked in real time Fall into detection.
In the present embodiment, equipment management system can connect host computer, slave computer, be collected simultaneously arrangement production, detection data, Equipment management system comprises the following modules:
Light source camera control module: the periodic light on and off of light source, stroboscopic are realized by software or hardware trigger interface.
Machine control modules: automatic movement is realized for angle direction, the distance controlling of light source;
Scheme memory module: the different types of polishing mode of record storage;
External communication module: there is the interface externally communicated with PLC and manipulator and the integral machine of entire combination;
Product defects categorization module: for realizing the differentiation for defect classification;
The automatic categorization module of product: for realizing the classification for variety classes product;
Product quality warning module: real-time statistics are carried out for the defects count to product;
Real-time online rejects module: by the device for eliminating (such as manipulator) outside docking and by faulty goods/mixing It rejects in time.
The detection machine of hundreds of polishing scheme of analog of the present invention, configured with manual zoom (12-36mm) camera lens+program-controlled Light source+multifunctional workstation, while can recorde hundreds of camera position and corresponding polishing scheme, it can call cut at any time It changes.
Above embodiments are only exemplary embodiment of the present invention, are not used in the limitation present invention, protection scope of the present invention It is defined by the claims.Those skilled in the art can within the spirit and scope of the present invention make respectively the present invention Kind modification or equivalent replacement, this modification or equivalent replacement also should be regarded as being within the scope of the present invention.

Claims (6)

1. a kind of defects detection training machine based on deep learning, which is characterized in that including industrial personal computer, bottom light source, turntable, Distance adjusts bogie, side light source, programmable light source and camera lens assembly, is equipped with deep learning software in the industrial personal computer And equipment management system, the bottom light source are located at the lower section of turntable, the distance adjusts rotating turret and is located above the turntable, institute It states programmable light source and is connect by light source shelf with apart from adjusting bogie, the camera lens assembly connects with apart from adjusting bogie It connects, and the lower end camera lens of the camera lens assembly downward and is directed at the upper end outlet of programmable light source.
2. the defects detection training machine according to claim 1 based on deep learning, which is characterized in that the distance is adjusted Section bogie is linear mould group.
3. the defects detection training machine according to claim 1 based on deep learning, which is characterized in that the bottom light Source and side light source are luminescent panel.
4. a kind of application method of the defects detection training machine as described in any one of claims 1 to 3 based on deep learning, It is characterized by comprising the following steps:
1) light emitting region and the brightness of programmable light source are adjusted, by the equipment management system of industrial personal computer to obtain image data;
2) image data that will acquire carries out algorithm training, and the algorithm that training is generated by the deep learning software of industrial personal computer Model detects workpiece for measurement;
3) classification and statistics of defects count, defect classification are carried out to workpiece for measurement, and pass through manipulator crawl qualified product and non- Qualified product.
5. the application method of the defects detection training machine according to claim 4 based on deep learning, which is characterized in that The deep learning software includes:
Labeling module: for realizing the labeling of the image data to camera and camera lens acquisition, and type is carried out to defect image Mark, is the data source of post deep learning training;
Training module: learning the data marked, to obtain optimal disaggregated model;
Cloud storage module: the image at scene is returned in Cloud Server, the data-optimized of later period is carried out;
Test module: trained model is loaded into, and the image acquired by camera and lens assembly carries out real-time defect inspection It surveys.
6. the application method of the defects detection training machine according to claim 5 based on deep learning, which is characterized in that The equipment management system comprises the following modules:
Light source camera control module: the periodic light on and off of light source, stroboscopic are realized by software or hardware trigger interface.
Machine control modules: for controlling angle direction, the distance of light source to realize automatic movement;
Scheme memory module: it is used for the different types of polishing mode of record storage;
External communication module: there is the interface externally communicated with PLC and manipulator and the integral machine of entire combination;
The automatic categorization module of product: for variety classes product classification;
Product quality warning module: it is used for real-time statistics defects count;
Real-time online rejects module: for rejecting faulty goods/mixing by the device for eliminating outside docking.
CN201910385380.8A 2019-05-09 2019-05-09 Defects detection training machine and its application method based on deep learning Pending CN110335239A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111487250A (en) * 2020-04-02 2020-08-04 苏州奥创智能科技有限公司 Intelligent visual detection method and system applied to injection molding defective product detection
CN112907529A (en) * 2021-02-09 2021-06-04 南京航空航天大学 Image-based woven preform defect detection method and device

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102222452A (en) * 2011-05-31 2011-10-19 光远科技股份有限公司 Advertising light box and method for controlling light source of same
CN106770307A (en) * 2016-09-27 2017-05-31 西京学院 A kind of electronic product appearance surfaces defect detecting device and its detection method
CN206683637U (en) * 2017-03-17 2017-11-28 苏州富鑫林光电科技有限公司 AOI vision-based detection bowls light source matches somebody with somebody backlight testing agency
CN208206822U (en) * 2018-04-10 2018-12-07 深圳市嘉立创科技发展有限公司 Pcb board defect automatic checkout system based on machine vision
CN109142371A (en) * 2018-07-31 2019-01-04 华南理工大学 High density flexible exterior substrate defect detecting system and method based on deep learning
CN109242830A (en) * 2018-08-18 2019-01-18 苏州翔升人工智能科技有限公司 A kind of machine vision technique detection method based on deep learning

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102222452A (en) * 2011-05-31 2011-10-19 光远科技股份有限公司 Advertising light box and method for controlling light source of same
CN106770307A (en) * 2016-09-27 2017-05-31 西京学院 A kind of electronic product appearance surfaces defect detecting device and its detection method
CN206683637U (en) * 2017-03-17 2017-11-28 苏州富鑫林光电科技有限公司 AOI vision-based detection bowls light source matches somebody with somebody backlight testing agency
CN208206822U (en) * 2018-04-10 2018-12-07 深圳市嘉立创科技发展有限公司 Pcb board defect automatic checkout system based on machine vision
CN109142371A (en) * 2018-07-31 2019-01-04 华南理工大学 High density flexible exterior substrate defect detecting system and method based on deep learning
CN109242830A (en) * 2018-08-18 2019-01-18 苏州翔升人工智能科技有限公司 A kind of machine vision technique detection method based on deep learning

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111487250A (en) * 2020-04-02 2020-08-04 苏州奥创智能科技有限公司 Intelligent visual detection method and system applied to injection molding defective product detection
CN112907529A (en) * 2021-02-09 2021-06-04 南京航空航天大学 Image-based woven preform defect detection method and device

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Effective date of registration: 20220208

Address after: Building 95-1, lingshangjiayuan, north of West 7th Road, Center Avenue, airport logistics processing zone, Tianjin Free Trade Zone (Airport Economic Zone), Binhai New Area, Tianjin

Applicant after: FITOW (TIANJIN) DETECTION TECHNOLOGY CO.,LTD.

Address before: 300300 Room 518, intelligent networked automobile industry research institute, No. 15, Hongcheng Road, Huaming high tech Industrial Zone, Huaming street, Dongli District, Tianjin

Applicant before: FEITE (TIANJIN) INTELLIGENT TECHNOLOGY Co.,Ltd.