EP4281930A1 - Contrôle visuel d' un élément se déplaçant sur une ligne de production - Google Patents
Contrôle visuel d' un élément se déplaçant sur une ligne de productionInfo
- Publication number
- EP4281930A1 EP4281930A1 EP22705424.4A EP22705424A EP4281930A1 EP 4281930 A1 EP4281930 A1 EP 4281930A1 EP 22705424 A EP22705424 A EP 22705424A EP 4281930 A1 EP4281930 A1 EP 4281930A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- checked
- image
- video sequence
- generation
- conformity
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000004519 manufacturing process Methods 0.000 title claims description 38
- 238000012360 testing method Methods 0.000 claims abstract description 30
- 239000013598 vector Substances 0.000 claims abstract description 25
- 238000000605 extraction Methods 0.000 claims abstract description 10
- 230000000007 visual effect Effects 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000000638 solvent extraction Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims 1
- 238000011179 visual inspection Methods 0.000 abstract description 6
- 238000010801 machine learning Methods 0.000 abstract description 4
- 238000012545 processing Methods 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000003908 quality control method Methods 0.000 description 3
- 238000010191 image analysis Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 238000004377 microelectronic Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8803—Visual inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
Definitions
- the present invention relates to the field of quality control in an industrial environment.
- the present invention relates more particularly to a solution for the visual control of products being manufactured on a production line.
- One of the objectives of the present invention is to allow easy and rapid deployment of a visual system for controlling a production line in an industrial environment.
- production line within the meaning of the present invention, is meant throughout the following description a line (or chain) comprising all or part of the manufacturing operations necessary for the production of a manufactured product.
- the products being manufactured will therefore move on this production line from one station to another via a conveyor, conveyor belt or equivalent.
- Visual inspection traditionally refers to automated image analysis techniques to determine the characteristics of objects moving on a production line.
- Control using machine vision enables early error detection and high throughput of production lines.
- machine vision systems tend to be more consistent and are not prone to fatigue, illness, and stress that can impact performance.
- the present invention aims to improve the situation described above
- the present invention aims in particular to remedy at least one of the various technical problems mentioned above by proposing a visual control with automatic learning.
- the object of the present invention thus relates, according to a first aspect, to a method of visual inspection of an element moving on a production line.
- the method is implemented by computer means and comprises the following steps:
- a generation of a conformity test by generating from the at least one first image comprising the at least one element to be checked a reference characteristic vector, said vector comprising a set of characteristic values representative of an element of reference ;
- a learning technique is available making it possible, during a first phase, to automate the detection of anomaly by constructing a conformity test according to methods of machine learning type artificial intelligence.
- the generation of the conformity test comprises a plotting of characteristic points of the at least one element to be checked in order to follow the at least one element to be checked in the first video sequence and to collect a plurality of elements to be checked.
- the generation of the conformity test is carried out by learning on the plurality of elements to be checked.
- the generation of the conformity test includes a normalization of the first image.
- the generation of the conformity test includes a semantic analysis of the at least one element to be checked according to a plurality of determined descriptors.
- the reference feature vector includes values representative of the semantics of the image (shape, texture, etc.).
- the generation of the conformity test comprises a supervised partitioning (or “supervised clustering”) of the characteristic values of each characteristic vector to determine at least one cluster (or class).
- the generation of the conformity test includes a calculation of similarity scores as a function of the distances between the characteristic vectors and the center of each cluster.
- the first extraction comprises a display of the first video sequence on a tactile graphical interface and a selection of said at least one first image by manually interacting with the interface.
- this selection can also be made via a simple graphical interface.
- the step of selecting the trigger element comprises a first selection of an area of interest on the at least one first image by manually interacting with the interface.
- the step of selecting the element to be checked comprises a second selection of a sub-zone of interest in the zone of interest corresponding to the trigger element, said second selection being carried out by manually interacting with the interface.
- this selection can also be made via a simple graphical interface.
- the use of a touch graphic interface facilitates the user experience who can intuitively select the image, the trigger and the element to be controlled via the touch screen.
- the verification of the level of conformity comprises, in the event of detection of an anomaly, generation of a warning signal.
- the object of the present invention relates to a computer program which comprises instructions adapted for the execution of the steps of the method according to the first aspect of the invention, this in particular when the computer program is executed by at least one processor.
- Such a computer program can use any programming language, and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other desirable form.
- the object of the present invention relates to a recording medium readable by a computer on which is recorded a computer program comprising instructions for the execution of the steps of the method according to the first aspect of the invention. .
- the recording medium can be any entity or device capable of storing the program.
- the medium may comprise a storage means, such as a ROM memory, a CD-ROM or a ROM memory of the microelectronic circuit type, or even a magnetic recording means or a hard disk.
- this recording medium can also be a transmissible medium such as an electrical or optical signal, such a signal being able to be conveyed via an electrical or optical cable, by conventional or hertzian radio or by self-directed laser beam or by other ways.
- the computer program according to the invention can in particular be downloaded from an Internet-type network.
- the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to execute or to be used in the execution of the method in question.
- the object of the present invention relates, according to a fourth aspect, to a computer system for the visual control of an element moving on a production line, said system comprising computer means configured for an implementation of the steps of the method as described above. -above.
- the object of the present invention relates, according to a fifth aspect, to a suitcase on wheels comprising a visual control computer system as described above.
- the operator has an easy-to-use tool that can be deployed in a few minutes without prior knowledge of the industrial environment in which he operates.
- FIG. 1 is a flowchart illustrating the different steps of the method according to an example embodiment of the invention
- FIG. 2 represents a schematic view of a visual control system according to an exemplary embodiment of the invention.
- a suitcase comprising a visual control system according to an exemplary embodiment of the present invention as well as the method associated with it will now be described in the following with reference jointly to FIGS. 1 and 2.
- one of the objectives is therefore to propose a solution which allows the industry to support quality control and conformity in complete autonomy by providing the industry, and in particular the automotive industry, with a visual control tool which is simple to deploy and does not require the intervention of a specialized operator.
- Such a transportable suitcase 200 is easy to use; it allows easy movement of all the components of the System 100 to the precise location where the operator wishes to establish his PC control point to control the LP production line.
- this suitcase 200 includes the various technical and computer means of the system 100 allowing proper implementation of the invention.
- the system 100 includes:
- - Means 10 for capturing images here called a camera
- a graphic interface 30 possibly tactile 30 such as for example a digital tablet type
- the system 100 also comprises a communication interface 70 capable of establishing wireless communication of the type, for example radio frequency, 3G, 4G or 5G (or equivalent) or deploying other technologies such as Wifi®, Bluetooth®, ZigBee® or RFID.
- a communication interface 70 capable of establishing wireless communication of the type, for example radio frequency, 3G, 4G or 5G (or equivalent) or deploying other technologies such as Wifi®, Bluetooth®, ZigBee® or RFID.
- Such an interface makes it possible in particular to ensure communication with the tablet 30 and possibly with the camera 10 if necessary.
- the processor(s) 40 configured for the execution of the instructions of the embedded software(s).
- Processor 40 may include onboard memory, an interface input/output, and various circuits known to those skilled in the art. It can be a processor (or a microprocessor) of the type CPU, GPU, TPU, FPGA, etc. these are examples of possible implementations for those skilled in the art.
- the concept underlying the present invention is based on the exploitation of technologies relating to artificial intelligence to detect in real time, precisely and automatically the anomalies present on the various products moving on the line. LP production.
- a learning phase PO is thus provided in the context of the present invention.
- this suitcase 200 he takes the camera 10 of the high resolution type for example and positions the latter according to a determined control point PC so that the field of view of the camera 10 includes the production line LP.
- the operator then initiates the learning phase PO at a determined learning instant by launching a first acquisition S0 of a first video sequence SV1.
- This first video sequence SV1 then comprises a stream of images comprising a plurality of objects moving on the production line LP.
- Preliminary steps for adjusting the sharpness, brightness and/or resolution can optionally be provided to improve the quality of the image. This may be desirable in certain industrial environments, especially on production lines in industries that operate day and night and in which the lighting can often be poor.
- the graphic interface comprises image processing means making it possible to control (continuously or discontinuously) the quality of the image (variation of light or equivalents, etc.) to detect anomalies early and remove any ambiguity. A signal is then emitted to raise the alert in the event of an anomaly or not. We also talk about disambiguation.
- This first sequence SV 1 is then recorded on storage means 20 of the volatile at/or non-volatile memory type and/or on a memory storage device which can include volatile and/or non-volatile memory, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic or optical disk.
- volatile and/or non-volatile memory such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic or optical disk.
- the operator then displays on the tablet 30 this first video sequence SV1 to extract therefrom during a step SI at least a first image II.
- This extraction SI is done by interaction with the tablet 30 by selecting said first image II of the sequence SV1; alternatively, this SI extraction can be done automatically by software means which analyze the flow of images and select one or more images according to a determined quality criterion (sharpness for example or others).
- the operator visualizes on his tablet 30 an image II comprising at least one object moving on the production line LP.
- This selection step S2 is carried out via the tablet 30:
- the operator selects S2_l on the first image II an area of interest corresponding to the trigger by manually interacting with the interface 30.
- This selection S2_l is made for example by drawing with your finger on the screen of the tablet a frame including the TRI trigger.
- a TRI trigger corresponds to an object or part of an object always present on the element to be controlled; here it can be, for example, the front part of the car or a wheel.
- a TRI trigger subsequently makes it possible to trigger the capture and analysis of images in the PI production phase.
- the operator selects S2_2 a sub-zone of interest corresponding to the element to be checked E, for example the rear-view mirror to be checked or the rim to be checked.
- the TRI trigger object must not be the element to be checked E, because it is desirable that the image capture be triggered even if the element to be checked E is missing or abnormal.
- this selection S2_2 is made by drawing with the finger on the screen a frame comprising the element to be checked in the previously selected zone of interest.
- the concept of selection of trigger TRI and element to be controlled E is desirable to improve the robustness of the algorithm and to limit the processing time and the computing resources in the production phase PI.
- This generation of conformity test is characteristic of the present invention; this is carried out by learning techniques of the Machine Learning type implemented by a processor 40 coupled to image processing means 50 included in the system 100 and integrated in the suitcase 200.
- the generation S3 of the conformity test includes a tracing of characteristic points of the element to be checked E.
- a tracing aims to follow the element to be checked E in the first video sequence SV1 in order to thus collect a plurality of items to check.
- the processor can then collect a plurality of elements to be checked E from the same video sequence SV1, which therefore makes it possible from a single sequence to have enough data to have a robust test.
- a normalization of the first image II can be provided. Then, a transformation of the first image II into a list of “features” type descriptors is provided to allow a semantic analysis of the element to be checked E according to this list of descriptors: the generation of the conformity test thus comprises a generation at from each element to be checked a reference characteristic vector V_REF comprising a set of characteristic values representative of each reference element.
- the result, for an image, is therefore a list of scores of similarities corresponding to the diversities (red mirror 95%, blue mirror 0%).
- a threshold on this score is set (80% by default, empirical), below which the class found cannot be considered as nominal: (example red mirror 32%, blue mirror 1%: anomaly, we do not recognize anything sufficiently ).
- the similarity index is then presented as a score that has been designed to give a “human” and intuitive meaning to the understanding of the result. It is understood here that an overall threshold applies to each class. For each analysis of an element to be checked and independently of the class detected, if the similarity score is below the global threshold, an alert is raised.
- the threshold by class allows to refine the definition of the threshold for each of the classes of the control point. Thus, for each analysis, if the similarity score is below the detected class threshold, an alert will be raised.
- the table under each threshold is the confusion matrix, it facilitates the definition of the threshold because its variation modifies the content of the confusion matrix
- This phase P 1 more particularly comprises a second acquisition S4 at a current instant, after the learning instant, of a second video sequence SV2.
- This acquisition S4 is carried out here using the camera 10 arranged according to the control point PC.
- Image processing is performed on this second video sequence to recognize S5 in real time the TRI trigger element in this second video sequence SV2, for example the front part of the vehicle or a wheel.
- the processor generates a trigger signal SD which activates the image processing circuit 50 so as to extract during a step S6 one or more second images 12 from this second video sequence SV2; this or these second images which each include the trigger element TRI (for example the front part of the car or the wheel of the car) are then analyzed S 7 by image processing to detect the element or elements to be checked E (rear view mirror or rim).
- a trigger signal SD which activates the image processing circuit 50 so as to extract during a step S6 one or more second images 12 from this second video sequence SV2; this or these second images which each include the trigger element TRI (for example the front part of the car or the wheel of the car) are then analyzed S 7 by image processing to detect the element or elements to be checked E (rear view mirror or rim).
- a determination S8 of a characteristic vector V_E is then provided for each element to be checked E detected, and it is then possible to verify during a step S9 a level of conformity by application of the conformity test by comparing the characteristic vector V_E of each element to control E.
- a warning signal SA intended for the warning means 60 of so that the lens (for example a flashing light) emits a visual and/or audible signal to warn the operators on the production line.
- the present invention by providing a portable suitcase 200 makes possible the deployment of an automatic control solution by vision in an industrial environment, including in particular assembly lines, conveyors, treadmills.
- an industrial environment including in particular assembly lines, conveyors, treadmills.
- the 100 system on board this 200 case is ergonomic and easy to use; it also makes it possible to create and deploy industrial visual analyzes in less than 30 minutes, without involving specialized operators unlike other state-of-the-art solutions.
- the suitcase 200 as proposed in the context of the present invention in fact embeds all the technical and computer means making it possible to easily and quickly set up an automatic image analysis system 100 (selective detection of the assembly and/or analysis (e.g. sorting, fault detection) on a production line, without programming, without computer coding, without scientific parameterization, without prior technical knowledge of the industrial environment.
- an automatic image analysis system 100 selective detection of the assembly and/or analysis (e.g. sorting, fault detection) on a production line, without programming, without computer coding, without scientific parameterization, without prior technical knowledge of the industrial environment.
- connection to a third-party server remains possible to download a predetermined compliance test in order to carry out a specific compliance test, for example to identify an anomaly that is difficult to detect.
- the system 100 of the suitcase 200 can include power supply means (not shown here) of the rechargeable battery type.
- a power cable can also be provided for a mains power supply.
- Camera 10 is connected to system 100 by a POE type Ethernet cable or by communication interface 70.
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Quality & Reliability (AREA)
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Multimedia (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
FR2100582A FR3119038B1 (fr) | 2021-01-21 | 2021-01-21 | Contrôle visuel d’un élément se déplaçant sur une ligne de production |
PCT/FR2022/050102 WO2022157452A1 (fr) | 2021-01-21 | 2022-01-19 | Contrôle visuel d' un élément se déplaçant sur une ligne de production |
Publications (1)
Publication Number | Publication Date |
---|---|
EP4281930A1 true EP4281930A1 (fr) | 2023-11-29 |
Family
ID=74871649
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP22705424.4A Pending EP4281930A1 (fr) | 2021-01-21 | 2022-01-19 | Contrôle visuel d' un élément se déplaçant sur une ligne de production |
Country Status (4)
Country | Link |
---|---|
EP (1) | EP4281930A1 (fr) |
KR (1) | KR20230133315A (fr) |
FR (1) | FR3119038B1 (fr) |
WO (1) | WO2022157452A1 (fr) |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6175652B1 (en) | 1997-12-31 | 2001-01-16 | Cognex Corporation | Machine vision system for analyzing features based on multiple object images |
US6567538B1 (en) * | 1999-08-02 | 2003-05-20 | The United States Of America As Represented By The Secretary Of Agriculture | Real time measurement system for seed cotton or lint |
US6483935B1 (en) | 1999-10-29 | 2002-11-19 | Cognex Corporation | System and method for counting parts in multiple fields of view using machine vision |
US20130070113A1 (en) | 2011-09-13 | 2013-03-21 | Cognex Corporation | Master and Slave Machine Vision System |
FR3030846B1 (fr) * | 2014-12-23 | 2017-12-29 | Commissariat Energie Atomique | Representation semantique du contenu d'une image |
-
2021
- 2021-01-21 FR FR2100582A patent/FR3119038B1/fr active Active
-
2022
- 2022-01-19 KR KR1020237026558A patent/KR20230133315A/ko unknown
- 2022-01-19 WO PCT/FR2022/050102 patent/WO2022157452A1/fr active Application Filing
- 2022-01-19 EP EP22705424.4A patent/EP4281930A1/fr active Pending
Also Published As
Publication number | Publication date |
---|---|
FR3119038B1 (fr) | 2023-03-17 |
KR20230133315A (ko) | 2023-09-19 |
FR3119038A1 (fr) | 2022-07-22 |
WO2022157452A1 (fr) | 2022-07-28 |
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