CN112347866A - System and method for realizing attachment assembly fool-proof detection processing based on machine vision - Google Patents
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Abstract
The invention relates to a system for realizing attachment assembly fool-proof detection processing based on machine vision, wherein a video picture frame information acquisition module is used for reading product picture frame information and respectively sending the product picture frame information to a target detection module and a bar code identification module, and a list comparison module is used for carrying out consistency comparison on the acquired actual information and a standard list and carrying out subsequent processing according to a comparison result. The invention also relates to a method for realizing attachment assembly fool-proof detection processing based on machine vision by using the system. By adopting the system and the method for realizing the foolproof detection processing of accessory assembly based on machine vision, the production station assembly line monitoring module is effectively utilized to automatically identify the type and quantity information of accessories of a product, the standard list of the type of the product and the corresponding accessory is obtained according to the product packaging bar code, and whether the type and quantity of the accessories are matched with the standard list or not is automatically matched, so that the foolproof detection function of accessories of intelligent and flexible product production is realized.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to the field of artificial intelligence vision, and specifically relates to a system and a method for realizing attachment assembly fool-proofing detection processing based on machine vision.
Background
In recent years, with the rapid development of artificial intelligence, the deep learning network is less influenced by environmental changes due to the fact that high-level features are formed by combining bottom-level features, and therefore breakthrough achievements are achieved in the field of computer vision, and especially in the great improvement of detection and identification of objects, the recognition accuracy of certain specific application scenes even exceeds that of human beings.
The intelligent manufacturing is an important direction for improving the level and competitiveness of the manufacturing industry in China at present, the problem of the manufacturing link is solved through artificial intelligence, and the improvement of the production efficiency is of great significance. In the assembly stage of product production, there is little difference in the product annex quantity of different models and the kind, and long-time manual packaging causes very easily to take by mistake, omits the problem, and how effectively solve and reduce this quality problem through workman's intelligence is a difficult problem that manufacturing type enterprise faces.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a system and a method for intelligently realizing attachment assembly fool-proof detection processing based on machine vision, which have high detection efficiency and are simple and convenient to operate.
In order to achieve the above object, the present invention provides a system and method for performing attachment assembly fool-proof detection processing based on machine vision, wherein the system comprises:
this system based on machine vision realizes annex equipment and prevents slow-witted detection processing, its key feature is, the system include:
the video image frame information acquisition module is used for reading the video stream from the production line monitoring module and acquiring corresponding product image frame information from the video stream;
the target detection module is used for analyzing and calculating the information of the types and the quantity of the actual product accessories according to the product picture frame information acquired from the video picture frame information acquisition module;
the bar code identification module is used for identifying a bar code of the product and calculating a corresponding bar code value according to the product picture frame information acquired from the video picture frame information acquisition module, sending an inquiry request to an MES system according to the bar code value and acquiring the model of the product and a corresponding accessory type standard list;
and the list comparison module is used for acquiring the actual product accessory type and quantity information from the target detection module, acquiring the product model and the corresponding accessory type standard list from the bar code identification module, comparing the actual product accessory type and quantity information, the product model and the corresponding accessory type standard list in a consistency manner, and performing subsequent processing according to the comparison result.
Preferably, the assembly line monitoring module is a camera module installed on an assembly line station.
The method for assembling and fool-proof detection processing of the accessory based on machine vision by using the system is mainly characterized by comprising the following steps:
(1) the video picture frame information acquisition module acquires product picture frame information and respectively sends the product picture frame information to the target detection module and the bar code identification module;
(2) the target detection module acquires corresponding product picture frame information, analyzes and calculates the information of the types and the number of the actual product accessories;
(3) the bar code identification module acquires the product picture frame information, identifies a product bar code and calculates a corresponding bar code value;
(4) the bar code identification module sends a query request to an MES system according to the bar code value to acquire the model of a product and a corresponding accessory type standard list;
(5) the list comparison module acquires the actual product accessory type and quantity information from the target detection module, acquires the product model and the corresponding accessory type standard list from the bar code identification module, and compares the actual product accessory type and quantity information, the product model and the corresponding accessory type standard list in a consistency manner;
(6) if the consistency comparison result is consistent, returning the qualified result of the assembly accessory; and if the result of the consistency comparison is inconsistent, returning the result that the assembly accessory is unqualified.
Preferably, the step (1) is specifically:
the video picture frame information acquisition module reads a video stream from the assembly line monitoring module, acquires corresponding product picture frame information from the video stream, and sends the product picture frame information to the target detection module and the bar code identification module.
Preferably, the step (2) comprises the following steps:
(2.1) the target detection module inputs the product picture frame information into a Mobile-SSD deep neural network;
and (2.2) the target detection module automatically analyzes and calculates the accessory types and the number of the actual products in the product picture frame information according to an algorithm model trained by the system in advance.
Preferably, the step (3) is specifically:
the bar code recognition module inputs the acquired product picture frame information into an image bar code recognition algorithm to automatically recognize the bar code value of the packing box of the product in the product picture frame information.
Preferably, the step (4) is specifically:
and the bar code identification module sends a query request to an MES (manufacturing execution system) through an HTTP GET API interface according to the bar code value to acquire the model of the product and a corresponding accessory type standard list.
By adopting the system and the method for realizing the foolproof detection processing of accessory assembly based on machine vision, the production station assembly line monitoring module is effectively utilized to automatically identify the type and quantity information of accessories of a product, the standard list of the type of the product and the corresponding accessory is obtained according to the product packaging bar code, and whether the type and quantity of the automatically matched accessories are consistent with the standard list or not is automatically matched, so that the problems of wrong taking and omission caused by low manual finishing efficiency in the production and assembly stage of the product are greatly reduced, manual operation is distinguished, the quality of finished product assembly is greatly improved, the foolproof detection function of accessories in intelligent and flexible product production is realized, and the innovation of breakthrough optimizing the production flow is brought to manufacturing enterprises.
Drawings
Fig. 1 is a schematic diagram of the overall architecture of the system for implementing attachment assembly fool-proofing detection processing based on machine vision according to the present invention.
FIG. 2 is a schematic diagram of the overall operation principle of the system for implementing attachment assembly fool-proof detection processing based on machine vision according to the present invention.
FIG. 3 is a flowchart illustrating a method for performing fool-proof detection processing of accessory assembly based on machine vision according to the present invention.
Detailed Description
In order to more clearly describe the technical contents of the present invention, the following further description is given in conjunction with specific embodiments.
Referring to fig. 1 and fig. 2, the system for implementing fool-proof detection processing of accessory assembly based on machine vision according to the present invention includes:
the video image frame information acquisition module is used for reading the video stream from the production line monitoring module and acquiring corresponding product image frame information from the video stream;
the target detection module is used for analyzing and calculating the information of the types and the quantity of the actual product accessories according to the product picture frame information acquired from the video picture frame information acquisition module;
the bar code identification module is used for identifying a bar code of the product and calculating a corresponding bar code value according to the product picture frame information acquired from the video picture frame information acquisition module, sending an inquiry request to an MES system according to the bar code value and acquiring the model of the product and a corresponding accessory type standard list;
and the list comparison module is used for acquiring the actual product accessory type and quantity information from the target detection module, acquiring the product model and the corresponding accessory type standard list from the bar code identification module, comparing the actual product accessory type and quantity information, the product model and the corresponding accessory type standard list in a consistency manner, and performing subsequent processing according to the comparison result.
As a preferred embodiment of the invention, the production line monitoring module is a camera module arranged on a production line station.
Referring to fig. 3, the method for detecting fool-proofing of machine vision-based accessory assembly using the system of the present invention is characterized by comprising the following steps:
(1) the video picture frame information acquisition module acquires product picture frame information and respectively sends the product picture frame information to the target detection module and the bar code identification module;
(2) the target detection module acquires corresponding product picture frame information, analyzes and calculates the information of the types and the number of the actual product accessories;
(3) the bar code identification module acquires the product picture frame information, identifies a product bar code and calculates a corresponding bar code value;
(4) the bar code identification module sends a query request to an MES system according to the bar code value to acquire the model of a product and a corresponding accessory type standard list;
(5) the list comparison module acquires the actual product accessory type and quantity information from the target detection module, acquires the product model and the corresponding accessory type standard list from the bar code identification module, and compares the actual product accessory type and quantity information, the product model and the corresponding accessory type standard list in a consistency manner;
(6) if the consistency comparison result is consistent, returning the qualified result of the assembly accessory; and if the result of the consistency comparison is inconsistent, returning the result that the assembly accessory is unqualified.
As a preferred embodiment of the present invention, the step (1) specifically comprises:
the video picture frame information acquisition module reads a video stream from the assembly line monitoring module, acquires corresponding product picture frame information from the video stream, and sends the product picture frame information to the target detection module and the bar code identification module.
As a preferred embodiment of the present invention, the step (2) comprises the steps of:
(2.1) the target detection module inputs the product picture frame information into a Mobile-SSD deep neural network;
and (2.2) the target detection module automatically analyzes and calculates the accessory types and the number of the actual products in the product picture frame information according to an algorithm model trained by the system in advance.
As a preferred embodiment of the present invention, the step (3) specifically comprises:
the bar code recognition module inputs the acquired product picture frame information into an image bar code recognition algorithm to automatically recognize the bar code value of the packing box of the product in the product picture frame information.
As a preferred embodiment of the present invention, the step (4) specifically comprises:
and the bar code identification module sends a query request to an MES (manufacturing execution system) through an HTTP GET API interface according to the bar code value to acquire the model of the product and a corresponding accessory type standard list.
By adopting the system and the method for realizing the foolproof detection processing of accessory assembly based on machine vision, the production station assembly line monitoring module is effectively utilized to automatically identify the type and quantity information of accessories of a product, the standard list of the type of the product and the corresponding accessory is obtained according to the product packaging bar code, and whether the type and quantity of the automatically matched accessories are consistent with the standard list or not is automatically matched, so that the problems of wrong taking and omission caused by low manual finishing efficiency in the production and assembly stage of the product are greatly reduced, manual operation is distinguished, the quality of finished product assembly is greatly improved, the foolproof detection function of accessories in intelligent and flexible product production is realized, and the innovation of breakthrough optimizing the production flow is brought to manufacturing enterprises.
In this specification, the invention has been described with reference to specific embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
Claims (7)
1. A system for realizing attachment assembly fool-proof detection processing based on machine vision is characterized by comprising:
the video image frame information acquisition module is used for reading the video stream from the production line monitoring module and acquiring corresponding product image frame information from the video stream;
the target detection module is used for analyzing and calculating the information of the types and the quantity of the actual product accessories according to the product picture frame information acquired from the video picture frame information acquisition module;
the bar code identification module is used for identifying a bar code of the product and calculating a corresponding bar code value according to the product picture frame information acquired from the video picture frame information acquisition module, sending an inquiry request to an MES system according to the bar code value and acquiring the model of the product and a corresponding accessory type standard list;
and the list comparison module is used for acquiring the actual product accessory type and quantity information from the target detection module, acquiring the product model and the corresponding accessory type standard list from the bar code identification module, comparing the actual product accessory type and quantity information, the product model and the corresponding accessory type standard list in a consistency manner, and performing subsequent processing according to the comparison result.
2. The system for performing attachment assembly fool-proofing detection processing based on machine vision of claim 1, wherein the assembly line monitoring module is a camera module installed on an assembly line station.
3. A method for implementing a machine vision based accessory assembly fool-proofing detection process using the system of claim 1, the method comprising the steps of:
(1) the video picture frame information acquisition module acquires product picture frame information and respectively sends the product picture frame information to the target detection module and the bar code identification module;
(2) the target detection module acquires corresponding product picture frame information, analyzes and calculates the information of the types and the number of the actual product accessories;
(3) the bar code identification module acquires the product picture frame information, identifies a product bar code and calculates a corresponding bar code value;
(4) the bar code identification module sends a query request to an MES system according to the bar code value to acquire the model of a product and a corresponding accessory type standard list;
(5) the list comparison module acquires the actual product accessory type and quantity information from the target detection module, acquires the product model and the corresponding accessory type standard list from the bar code identification module, and compares the actual product accessory type and quantity information, the product model and the corresponding accessory type standard list in a consistency manner;
(6) if the consistency comparison result is consistent, returning the qualified result of the assembly accessory; and if the result of the consistency comparison is inconsistent, returning the result that the assembly accessory is unqualified.
4. The method for machine-vision-based accessory assembly fool-proofing detection processing according to claim 3, wherein the step (1) is specifically as follows:
the video picture frame information acquisition module reads a video stream from the assembly line monitoring module, acquires corresponding product picture frame information from the video stream, and sends the product picture frame information to the target detection module and the bar code identification module.
5. The method for machine-vision-based accessory assembly fool-proofing detection processing of claim 3, wherein said step (2) comprises the steps of:
(2.1) the target detection module inputs the product picture frame information into a Mobile-SSD deep neural network;
and (2.2) the target detection module automatically analyzes and calculates the accessory types and the number of the actual products in the product picture frame information according to an algorithm model trained by the system in advance.
6. The method for machine-vision-based accessory assembly fool-proofing detection processing according to claim 3, wherein the step (3) is specifically as follows:
the bar code recognition module inputs the acquired product picture frame information into an image bar code recognition algorithm to automatically recognize the bar code value of the packing box of the product in the product picture frame information.
7. The method for machine-vision-based accessory assembly fool-proofing detection processing according to claim 3, wherein the step (4) is specifically as follows:
and the bar code identification module sends a query request to an MES (manufacturing execution system) through an HTTP GET API interface according to the bar code value to acquire the model of the product and a corresponding accessory type standard list.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113420852A (en) * | 2021-05-17 | 2021-09-21 | 东风电驱动系统有限公司 | Error-proofing method, tracing method, system and storage medium for assembly of PCBA (printed circuit board assembly) |
| CN113436182A (en) * | 2021-07-08 | 2021-09-24 | 揭阳市汇宝昌电器有限公司 | Rapid visual detection and identification method for end cover assembly quality of miniature direct current motor |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103745198A (en) * | 2013-12-30 | 2014-04-23 | 中云智慧（北京）科技有限公司 | Method and device for assisting identification and comparison of lines and mails |
| CN106056020A (en) * | 2016-06-01 | 2016-10-26 | 东莞理工学院 | Bar code detection and recognition method and system based on machine vision system |
| CN108861420A (en) * | 2018-06-25 | 2018-11-23 | 芜湖美智空调设备有限公司 | Fool proof feeding control method and device, computer readable storage medium |
| CN110532825A (en) * | 2019-08-21 | 2019-12-03 | 厦门壹普智慧科技有限公司 | A kind of bar code identifying device and method based on artificial intelligence target detection |
| CN110929083A (en) * | 2019-12-11 | 2020-03-27 | 苏州榛子物联技术有限公司 | Visual footwear identification method based on artificial intelligence |
| CN111382622A (en) * | 2018-12-28 | 2020-07-07 | 泰芯科技（杭州）有限公司 | Medicine identification system based on deep learning and implementation method thereof |
| CN111582417A (en) * | 2020-05-13 | 2020-08-25 | 浪潮商用机器有限公司 | Attachment fool-proofing method and system |
-
2020
- 2020-10-22 CN CN202011138822.8A patent/CN112347866A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103745198A (en) * | 2013-12-30 | 2014-04-23 | 中云智慧（北京）科技有限公司 | Method and device for assisting identification and comparison of lines and mails |
| CN106056020A (en) * | 2016-06-01 | 2016-10-26 | 东莞理工学院 | Bar code detection and recognition method and system based on machine vision system |
| CN108861420A (en) * | 2018-06-25 | 2018-11-23 | 芜湖美智空调设备有限公司 | Fool proof feeding control method and device, computer readable storage medium |
| CN111382622A (en) * | 2018-12-28 | 2020-07-07 | 泰芯科技（杭州）有限公司 | Medicine identification system based on deep learning and implementation method thereof |
| CN110532825A (en) * | 2019-08-21 | 2019-12-03 | 厦门壹普智慧科技有限公司 | A kind of bar code identifying device and method based on artificial intelligence target detection |
| CN110929083A (en) * | 2019-12-11 | 2020-03-27 | 苏州榛子物联技术有限公司 | Visual footwear identification method based on artificial intelligence |
| CN111582417A (en) * | 2020-05-13 | 2020-08-25 | 浪潮商用机器有限公司 | Attachment fool-proofing method and system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113420852A (en) * | 2021-05-17 | 2021-09-21 | 东风电驱动系统有限公司 | Error-proofing method, tracing method, system and storage medium for assembly of PCBA (printed circuit board assembly) |
| CN113436182A (en) * | 2021-07-08 | 2021-09-24 | 揭阳市汇宝昌电器有限公司 | Rapid visual detection and identification method for end cover assembly quality of miniature direct current motor |
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