CN201134038Y - On-line part recognition system based on machine vision - Google Patents
On-line part recognition system based on machine vision Download PDFInfo
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- CN201134038Y CN201134038Y CNU2007200827293U CN200720082729U CN201134038Y CN 201134038 Y CN201134038 Y CN 201134038Y CN U2007200827293 U CNU2007200827293 U CN U2007200827293U CN 200720082729 U CN200720082729 U CN 200720082729U CN 201134038 Y CN201134038 Y CN 201134038Y
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Abstract
The utility model discloses an online part recognition system based on machine vision, which comprises a collector that collects the images of workpieces needing to be recognized, an industrial computer that is connected with the collector and processes the collected image signals, and a host that sends down commands and program files to the industrial computer. The online part recognition system is characterized in that an online part image recognition module is integrated in the industrial computer, and the online part image recognition module comprises an input module that inputs collected image signals, an image processing recognition module that processes the input image signals and an output module that outputs the image signals after recognition processing. The parts of the utility model have high recognition efficiency, good accuracy and wide applicability.
Description
Technical field
The utility model relates to Parts Recognition systems technology field, definite say to relate to processing parts is converted into the image with individual features structure, and then be translated into PT CODE and shown and be sent in the control system of flexible manufacturing system with individual features.
Background technology
Along with the development of modern manufacturing industry, manufacture a product and develop towards the direction of short run, multiple class, dramatic change is just taking place in traditional manufacturing technology and production management pattern; Processing industry just towards at a high speed, the direction of high-accuracy, increasingly automated, intellectuality and flexibility develops.Therefore, flexible manufacturing system is that FMS (Flexible Manufacturing System) also arises at the historic moment, and has also occurred the trend to unmanned workshop, factory's development simultaneously.
Machine vision technique has advantages such as rapidity, repeatability, intellectuality, noncontact, on-the-spot antijamming capability are strong, and along with the development of intelligent manufacturing system, increasing FMS will introduce machine vision technique.At present, from flexible manufacturing system both domestic and external, also not supporting as yet machine vision technique, but from the point of long-term development, this technology will be applied in flexible manufacturing system (particularly unmanned workshop).
The utility model content
For solving the problems of the technologies described above, the utility model proposes and a kind ofly can satisfy the high online Parts Recognition system of unmanned execute-in-place, intellectuality, automatic controlling level based on machine vision, the utility model Parts Recognition efficient height, accuracy is good, and is widely applicable.
The technical solution adopted in the utility model is as follows:
A kind of online Parts Recognition system based on machine vision, comprise that the workpiece to needs identification carries out the collector of image acquisition, the industrial computer that is connected with described collector and the picture signal that collects is handled, give the main frame of described industrial computer transmitting order to lower levels and project documentation, it is characterized in that: be integrated with online part image identification module in the described industrial computer, described online part image identification module comprises the load module that the picture signal that collects is imported, to the Flame Image Process identification module handled of picture signal of input with will discern the output module that the picture signal after handling is exported; Described Flame Image Process identification module comprises described picture signal is carried out pretreated image pretreatment module, the fault processing module of handling during to the generation fault, the parameter of described image processing module being carried out parameter setting is provided with module, deposit the image library module of part image, and the images match module of extracting part image and the part image in this image and the image library being analyzed; Also be connected with human-computer interaction interface on described fault processing module and the images match module.
Described industrial computer is provided with the I/O interface, and industrial computer links to each other with main frame through described I/O interface.
Described industrial computer comprise to described picture signal amplify, filtering, sampling processing, and the picture signal after will handling writes the capture card of industrial computer.
Described collector is the industrial CCD camera.
Described human-computer interaction interface is input keyboard and the display that shows recognition result.
Principle of work of the present utility model is as follows:
Before discerning part automatically, the image that absorbs various parts generates image library.Before each work program was carried out, the project documentation according to main frame issues upgraded the interim parts library of work at present plan; During recognition system work, when the automatic trailer wagon that is loaded with part is delivered to the material loading station with supporting plate, workman's clamping workpiece, press the shooting start button then, the light-source system startup provides uniform light for system, begin shooting by the industrial CCD camera, image pick-up card in the industrial computer to the picture signal that the industrial CCD camera sends amplify, processing such as filtering, sampling, and picture signal is written in the internal memory of industrial computer, by online part image identification module the part image that collects is carried out Flame Image Process and identification then.On display, show recognition result at last, simultaneously by the I/O interface, the parts information that identifies is sent to supporting plate storehouse control system and main frame, main frame is arranged machining center according to recognition result again and is called corresponding nc program and pass to corresponding machining center, finishes the processing of corresponding part.
Advantage of the present utility model is:
1, the utility model adopts online part image identification module that the image of workpiece is discerned, handled, in conjunction with industrial CCD camera, capture card and main frame, thereby have except loading and unloading by manually finishing, all the other programs are all by the automatic advantage finished of control of total system, have satisfied people to unmanned execute-in-place, intellectuality, requirement that automatic controlling level is high; And the recognition efficiency height, accuracy is good, and is widely applicable, can discern workpiece kinds such as casing, axle, dish class.
2, the utlity model has human-computer interaction interface, the result of image recognition will return human-computer interaction interface, be convenient for people to see recognition result; system possesses self-diagnostic function; if promptly occurred in the system identification process unusually, system will provide information and take protection, corrective measure.
Description of drawings
Fig. 1 is an one-piece construction synoptic diagram of the present utility model
Fig. 2 is the structural principle block scheme of online part image identification module
Embodiment
A kind of online Parts Recognition system based on machine vision, comprise that the workpiece to needs identification carries out the collector 1 of image acquisition, the industrial computer 2 that is connected with described collector 1 and the picture signal that collects is handled, give the main frame 3 of described industrial computer 2 transmitting order to lower levels and project documentation, it is characterized in that: be integrated with online part image identification module 4 in the described industrial computer 2, described online part image identification module 4 comprises the load module 5 that the picture signal that collects is imported, to the Flame Image Process identification module 6 handled of picture signal of input with will discern the output module 7 that the picture signal after handling is exported; Described Flame Image Process identification module 6 comprises described picture signal is carried out pretreated image pretreatment module 8, the fault processing module of handling during to the generation fault 9, the parameter of described image processing module being carried out parameter setting is provided with module 10, deposit the image library module 11 of part image, and the images match module 12 of extracting part image and the part image in this image and the image library being analyzed; Also be connected with human-computer interaction interface 13 on described fault processing module 9 and the images match module 12.Described industrial computer 2 is provided with I/O interface 14, and industrial computer 2 links to each other with main frame 3 through described I/O interface 14.Described industrial computer 2 comprise to described picture signal amplify, filtering, sampling processing, and the picture signal after will handling writes the capture card 15 of industrial computer 2.Described collector 1 is the industrial CCD camera.Described human-computer interaction interface 13 is input keyboard and the display that shows recognition result.
Embodiment 2
As shown in Figure 1: the online Parts Recognition hardware system based on machine vision is that high-resolution battle array black and white industrial camera, capture card 15, light source 16, optical lens, I/O interface 14 etc. are formed by industrial computer 2, collector 1 mainly, and it is used for the online part in flexible manufacturing island is discerned.Online Parts Recognition software systems are matching used with hardware system, and it can be designed as and can move on Windows series operating system.
The software development kit of developing online Parts Recognition software systems use is: Matrox Mil7.5, use to such an extent that main programming language is C++, and programming tool is Microsoft Visual C++ 6.0withService Pack 6 and SQL.
Before discerning part automatically, the image that absorbs various parts generates image library.Before each work program was carried out, the project documentation according to main frame 3 issues upgraded the interim parts library of work at present plan; During recognition system work, when the automatic trailer wagon that is loaded with part is delivered to the material loading station with supporting plate, workman's clamping workpiece 17, press the shooting start button then, the light-source system startup provides uniform light for system, begin shooting by the industrial CCD camera, processing such as the picture signal that the 15 pairs of industrial CCD cameras of image pick-up card in the industrial computer 2 send is amplified, filtering, sampling, and picture signal is written in the internal memory of industrial computer 2, carry out Flame Image Process and identification by 4 pairs of part images that collect of online part image identification module then.On display, show recognition result at last, simultaneously by the I/O interface, the parts information that identifies is sent to supporting plate storehouse control system and main frame 3, main frame 3 is arranged machining center according to recognition result again and is called corresponding nc program and pass to corresponding machining center, finishes the processing of corresponding part.
Embodiment 3
When the part of new kind is installed to the loading and unloading station on flexible manufacturing island, at first adopt interpolation new parts function that its image mode in accordance with regulations is stored in the image library module 11, after determining that all kinds that are identified part have all been put into image library module 11, can carry out the ONLINE RECOGNITION work of part.
At first read current batch the production task of sending from main frame 3 and promptly need the Part No. and the production quantity of processing, extract the part image on the present loading and unloading station, this characteristics of image and the current batch of part image that is stored in the image library are analyzed, find out this title that is identified part and will process the program number of this part, and on system screen, demonstrate this title that is identified part and will process the program number of this part, simultaneously will process the program number of this part and be identified as in the control system that function signal sends to the flexible manufacturing island, after certain part processing identification quantity reaches the production quantity that production management issues, this part ONLINE RECOGNITION system sends the information that this kind part has machined to higher level's production management, so that receive new production task.
If the part image on the current loading and unloading station that extracts does not meet with the image that is stored in current batch of part in the image library, then this part ONLINE RECOGNITION system will send and report to the police and prompting, the control system on flexible manufacturing island just can not sent into this part in the next procedure and go like this, so just avoids the damage phenomenon of the equipment that produces to take place.
Claims (5)
1, a kind of online Parts Recognition system based on machine vision, comprise that the workpiece to needs identification carries out the collector (1) of image acquisition, the industrial computer (2) that is connected with described collector (1) and the picture signal that collects is handled, give the main frame (3) of described industrial computer (2) transmitting order to lower levels and project documentation, it is characterized in that: be integrated with online part image identification module (4) in the described industrial computer (2), described online part image identification module (4) comprises the load module (5) that the picture signal that collects is imported, to the Flame Image Process identification module (6) handled of picture signal of input with will discern the output module (7) that the picture signal after handling is exported; Described Flame Image Process identification module (6) comprises described picture signal is carried out pretreated image pretreatment module (8), the fault processing module of handling during to the generation fault (9), the parameter of described image processing module being carried out parameter setting is provided with module (10), deposit the image library module (11) of part image, and the images match module (12) of extracting part image and the part image in this image and the image library being analyzed; Also be connected with human-computer interaction interface (13) on described fault processing module (9) and the images match module (12).
2, the online Parts Recognition system based on machine vision according to claim 1, it is characterized in that: described industrial computer (2) is provided with I/O interface (14), and industrial computer (2) links to each other with main frame (3) through described I/O interface (14).
3, the online Parts Recognition system based on machine vision according to claim 1, it is characterized in that: described industrial computer (2) comprise to described picture signal amplify, filtering, sampling processing, and the picture signal after will handling writes the capture card (15) of industrial computer (2).
4, the online Parts Recognition system based on machine vision according to claim 1, it is characterized in that: described collector (1) is the industrial CCD camera.
5, the online Parts Recognition system based on machine vision according to claim 1 is characterized in that: described human-computer interaction interface (13) is for input keyboard and show the display of recognition result.
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| Application Number | Priority Date | Filing Date | Title |
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| CNU2007200827293U CN201134038Y (en) | 2007-12-27 | 2007-12-27 | On-line part recognition system based on machine vision |
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| CNU2007200827293U CN201134038Y (en) | 2007-12-27 | 2007-12-27 | On-line part recognition system based on machine vision |
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102837426A (en) * | 2012-09-29 | 2012-12-26 | 必诺机械（东莞）有限公司 | Welding device for identifying welding head category |
| CN103092136A (en) * | 2012-12-27 | 2013-05-08 | 中国人民解放军65185部队 | Pad processing method using mobile intelligent terminal to input pad geometric structure information |
| CN104281132A (en) * | 2014-09-24 | 2015-01-14 | 镇江市高等专科学校 | Information collection system and method for machine manufacturing workshop in production process based on machine vision |
| CN104375760A (en) * | 2014-10-29 | 2015-02-25 | 小米科技有限责任公司 | Information display method and device |
| CN104932431A (en) * | 2015-06-29 | 2015-09-23 | 遵义宏港机械有限公司 | Externally-added type numerically-controlled milling machine intelligent coding control method |
| CN104950815A (en) * | 2015-06-29 | 2015-09-30 | 遵义宏港机械有限公司 | Automatic coding system for numerically-controlled milling machine |
| CN105094056A (en) * | 2015-06-29 | 2015-11-25 | 遵义宏港机械有限公司 | Numerical control milling machine automation coding method |
| CN105338322A (en) * | 2015-11-19 | 2016-02-17 | 无锡港湾网络科技有限公司 | Machine vision experimental platform and application thereof |
| CN105388163A (en) * | 2015-10-28 | 2016-03-09 | 佛山市南海区广工大数控装备协同创新研究院 | Intelligent detection system for surface defects of ceramic tiles |
| CN106325213A (en) * | 2015-06-30 | 2017-01-11 | 遵义林棣科技发展有限公司 | Automatic encoding system of numerically controlled lathe |
| CN106325214A (en) * | 2015-06-30 | 2017-01-11 | 遵义林棣科技发展有限公司 | Automatic coding method for numerical control lathe |
| US10163033B2 (en) | 2016-12-13 | 2018-12-25 | Caterpillar Inc. | Vehicle classification and vehicle pose estimation |
| CN109409905A (en) * | 2018-09-28 | 2019-03-01 | 微创（上海）医疗机器人有限公司 | A kind of medical instrument automatic identification check system and method |
| CN109472467A (en) * | 2018-10-23 | 2019-03-15 | 佛山欧神诺云商科技有限公司 | A kind of automatic glazed tile Production Scheduling System |
| CN114494820A (en) * | 2021-12-07 | 2022-05-13 | 新拓三维技术(深圳)有限公司 | A system and method for intelligent identification of auto parts |
-
2007
- 2007-12-27 CN CNU2007200827293U patent/CN201134038Y/en not_active Expired - Fee Related
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102837426A (en) * | 2012-09-29 | 2012-12-26 | 必诺机械（东莞）有限公司 | Welding device for identifying welding head category |
| CN102837426B (en) * | 2012-09-29 | 2014-12-10 | 必诺机械（东莞）有限公司 | Welding device for identifying welding head category |
| CN103092136A (en) * | 2012-12-27 | 2013-05-08 | 中国人民解放军65185部队 | Pad processing method using mobile intelligent terminal to input pad geometric structure information |
| CN104281132A (en) * | 2014-09-24 | 2015-01-14 | 镇江市高等专科学校 | Information collection system and method for machine manufacturing workshop in production process based on machine vision |
| CN104375760B (en) * | 2014-10-29 | 2018-04-24 | 小米科技有限责任公司 | Method for information display and device |
| CN104375760A (en) * | 2014-10-29 | 2015-02-25 | 小米科技有限责任公司 | Information display method and device |
| CN104932431A (en) * | 2015-06-29 | 2015-09-23 | 遵义宏港机械有限公司 | Externally-added type numerically-controlled milling machine intelligent coding control method |
| CN104950815A (en) * | 2015-06-29 | 2015-09-30 | 遵义宏港机械有限公司 | Automatic coding system for numerically-controlled milling machine |
| CN105094056A (en) * | 2015-06-29 | 2015-11-25 | 遵义宏港机械有限公司 | Numerical control milling machine automation coding method |
| CN106325213A (en) * | 2015-06-30 | 2017-01-11 | 遵义林棣科技发展有限公司 | Automatic encoding system of numerically controlled lathe |
| CN106325214A (en) * | 2015-06-30 | 2017-01-11 | 遵义林棣科技发展有限公司 | Automatic coding method for numerical control lathe |
| CN105388163A (en) * | 2015-10-28 | 2016-03-09 | 佛山市南海区广工大数控装备协同创新研究院 | Intelligent detection system for surface defects of ceramic tiles |
| CN105338322A (en) * | 2015-11-19 | 2016-02-17 | 无锡港湾网络科技有限公司 | Machine vision experimental platform and application thereof |
| US10163033B2 (en) | 2016-12-13 | 2018-12-25 | Caterpillar Inc. | Vehicle classification and vehicle pose estimation |
| CN109409905A (en) * | 2018-09-28 | 2019-03-01 | 微创（上海）医疗机器人有限公司 | A kind of medical instrument automatic identification check system and method |
| CN109472467A (en) * | 2018-10-23 | 2019-03-15 | 佛山欧神诺云商科技有限公司 | A kind of automatic glazed tile Production Scheduling System |
| CN114494820A (en) * | 2021-12-07 | 2022-05-13 | 新拓三维技术(深圳)有限公司 | A system and method for intelligent identification of auto parts |
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Granted publication date: 20081015 Termination date: 20111227 |