EP4139839A1 - Procédé et dispositif d'inspection optique de récipients dans un système de traitement de boissons - Google Patents

Procédé et dispositif d'inspection optique de récipients dans un système de traitement de boissons

Info

Publication number
EP4139839A1
EP4139839A1 EP21716307.0A EP21716307A EP4139839A1 EP 4139839 A1 EP4139839 A1 EP 4139839A1 EP 21716307 A EP21716307 A EP 21716307A EP 4139839 A1 EP4139839 A1 EP 4139839A1
Authority
EP
European Patent Office
Prior art keywords
images
evaluation unit
containers
error
image processing
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
Application number
EP21716307.0A
Other languages
German (de)
English (en)
Inventor
Alexander Hewicker
Anton Niedermeier
Herbert Kolb
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Krones AG
Original Assignee
Krones AG
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Krones AG filed Critical Krones AG
Publication of EP4139839A1 publication Critical patent/EP4139839A1/fr
Pending legal-status Critical Current

Links

Classifications

    • 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
    • G01N21/90—Investigating the presence of flaws or contamination in a container or its contents
    • G01N21/9036—Investigating the presence of flaws or contamination in a container or its contents using arrays of emitters or receivers
    • 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
    • G01N21/8851—Scan 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
    • 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
    • G01N21/90—Investigating the presence of flaws or contamination in a container or its contents
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • G06V10/7747—Organisation of the process, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/776—Validation; Performance evaluation
    • 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
    • G01N21/8851—Scan 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
    • G01N2021/8854—Grading and classifying of flaws
    • G01N2021/888—Marking defects
    • 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
    • G01N21/8851—Scan 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
    • G01N2021/8883—Scan 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 involving the calculation of gauges, generating models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06T2207/30164—Workpiece; Machine component
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/06—Recognition of objects for industrial automation

Definitions

  • the invention relates to a method and a device for the optical inspection of containers in a beverage processing plant with the features of the preamble of claims 1 and 10, respectively.
  • such methods and devices are used to inspect the container for contamination and / or defects, for example.
  • the containers are transported with a conveyor as a container mass flow and recorded as camera images by an inspection unit arranged in the beverage processing system.
  • the camera images are then inspected for errors by a first evaluation unit using conventional image processing methods. If an error, such as contamination or a defect in a container, is recognized, it is cleaned or recycled.
  • such methods and devices for the optical inspection of containers are used in the side wall, base and / or fill level inspection of empty containers or containers already filled with a product. It is also conceivable that such methods and devices for the optical inspection of containers in beverage processing plants are used to detect processing errors during treatment in the beverage processing plant. For example, whether a container has fallen over during transport or a jam is forming.
  • the camera images are evaluated by conventional image processing methods with transformation, point, neighborhood, filter, histogram, threshold, brightness and / or contrast operations in order to detect image areas in the camera images that contain dirt and / or have errors.
  • the disadvantage here is that such neural or deep neural networks have to be trained with a training data set which comprises 1000-10,000 or even more marked images in which, for example, the errors are marked.
  • the marking is usually done by image processing experts and / or trained employees at the manufacturer of the beverage processing system. This is also time-consuming and costly.
  • the object of the present invention is therefore to provide a method and a device for the optical inspection of containers in a beverage processing plant, in which setting up the image processing method is less time-consuming and costly.
  • the invention provides a method for the optical inspection of containers in a beverage processing plant with the features of claim 1.
  • Advantageous embodiments are mentioned in the subclaims.
  • the camera images with the defective containers are classified as error images and the errors are assigned to the error images as error markings, and because those of the camera images with the containers found to be good are classified as error-free images, a large number of error images and error-free images can occur
  • Basis of the conventionally working image processing method are provided. This can be done, for example, in the beverage processing plant with container types in which the conventionally working image processing method has been set up and thus works particularly reliably.
  • a specific training data set is then compiled from the error images, the error markings and the error-free images, and the second evaluation unit is trained on site with the image processing method based on artificial intelligence.
  • the specific training data set can be provided automatically to the greatest possible extent, as a result of which the method is particularly time-efficient and therefore cost-effective.
  • the method for optical inspection can be assigned to a container manufacturing method, cleaning method, filling and / or closing method before, after or.
  • the method can be used in a full bottle or empty bottle inspection machine that includes the inspection unit.
  • the containers can be provided to hold beverages, food, hygiene articles, pastes, chemical, biological and / or pharmaceutical products.
  • the containers can be designed as bottles, in particular as plastic bottles or glass bottles.
  • Plastic bottles can in particular be PET, PEN, HD-PE or PP bottles. It is also conceivable that the containers are preforms for producing the plastic bottles. They can also be biodegradable containers or bottles, the main components of which are made from renewable raw materials such as sugar cane, wheat or corn.
  • the containers can be provided with a closure, for example with a crown cap, screw cap, zip fastener or the like.
  • the containers can also be present as empties, preferably without a closure.
  • the method is used for side wall, floor, mouth, content and / or filling height control of the container.
  • the errors can be soiling, such as foreign bodies, product residues, residues of labels and / or the like. It can also be defects, such as damage to the containers, in particular special cracks and / or chipped glass. It is also conceivable that the material locations are incorrectly produced, such as, for example, local material thinning and / or thickening. It is also conceivable that the method is used to inspect returned reusable containers and / or to monitor the transport of the containers as container mass flow and / or to monitor the processing of the containers in the beverage processing plant, for example to remove containers that have fallen over on the conveyor or a jam to recognize.
  • the containers can be transported with the transporter to the inspection unit as the container mass flow, preferably as a single-lane container mass flow. However, a multi-lane container mass flow is also conceivable.
  • the conveyor can comprise a carousel and / or a linear conveyor. It is conceivable, for example, that the transporter comprises a conveyor belt on which the containers are transported standing in an inspection area of the inspection unit. Also conceivable are receiving elements that accommodate one or more containers during transport.
  • the containers can also be transported held by side straps if, for example, the lighting shines through the container base and the camera inspects the base through the container mouth.
  • the inspection unit can be designed as an optical inspection unit, in particular with a lighting device and / or with a camera, in order to pass through and / or close the containers illuminate.
  • the light can be generated with at least one light source, for example with a light bulb, a fluorescent tube and / or with at least one LED in order to backlight a light exit surface.
  • the light can preferably be generated with a matrix of LEDs and emitted in the direction of the light exit surface.
  • the light exit surface can be made larger than the camera view of the container. It is also conceivable that the light exit surface only illuminates part of the camera view of the container.
  • the light exit surface can partially or completely diffuse the light.
  • the light exit surface can preferably comprise a diffusing screen with which the light from the at least one light source is diffusely scattered over a large area towards the camera. It is conceivable that the light is generated by the lighting device, then shines through the container and / or reflected therefrom and is then captured by the camera.
  • the camera can in each case capture a partial area of a container, exactly one container or several containers and optionally the light transmitted or reflected over them with an objective and with an image sensor.
  • the image sensor can be, for example, a CMOS or a CCD sensor. It is conceivable that the camera transmits the camera images with a data interface to the first and / or the second evaluation unit.
  • the containers can each be captured in the camera images from one or more image perspectives.
  • the camera can detect the polarization property, the intensity property, the color property and / or the phase property of the light for each pixel of the camera images.
  • the first and / or second evaluation unit can process the camera images with a signal processor and / or with a CPU (Central Processing Unit) and / or GPU (Graphics Processing Unit) and / or with a TPU (Tensor Processing Unit) and / or with a VPU (Vision Processing Unit). It is also conceivable that the first and / or second evaluation unit comprises a memory unit, one or more data interfaces, for example a network interface, a display unit and / or an input unit.
  • the conventionally operating image processing method and / or the image processing method operating on the basis of artificial intelligence can be present as a computer program product in the first and / or second evaluation unit, in particular in the respective memory unit.
  • “Conventionally working image processing method” can mean here that the conventionally working image processing method is not based on artificial intelligence. In particular, this can mean that the conventionally operating image processing method does not include a method step with a neural network, in particular no method step with a deep neural network. It is also conceivable that this means that the conventionally working image processing method evaluates the camera images with transformation, point, neighborhood, filter, histogram, threshold value, brightness and / or contrast operations in order to create image areas directly in the camera images to recognize who have the errors.
  • defect containers can be meant containers that contain a defect.
  • Error markings can mean here that a list of the error images with an assigned error description is provided. This can also mean that the error markings are entered in the metadata of the respective error images. In general, this can mean any indicator that the error image shows an error. This can also mean the error coordinates of the errors in the error images, for example the coordinate of a contamination.
  • approved containers can be meant containers without errors or with a tolerable deviation.
  • Image processing method working on the basis of artificial intelligence can mean here that the image processing method working on the basis of artificial intelligence comprises at least one method step with a neural network, in particular with a deep neural network.
  • a neural network in particular with a deep neural network.
  • it can be a so-called folding neural network with at least one folding layer and with a pooling layer.
  • “On site” can mean that the second evaluation unit is trained with the training data set on site at the operator of the beverage processing system, in particular on site in the beverage processing system.
  • the second evaluation unit trains with the image processing method based on artificial intelligence at a manufacturer of the beverage processing system with a generic training data set and that then in a second step the second evaluation unit with the image that works on the basis of artificial intelligence processing method is trained on site with the specific training data set.
  • the second evaluation unit can initially be trained with data available at the manufacturer of the beverage processing system and then further trained on site in the beverage processing system.
  • the manufacturer of the second evaluation unit can also mean the manufacturer of the beverage processing system, the inspection unit and / or the first evaluation unit.
  • the “generic training data set” can mean a compilation of error images, error markings and / or error-free images with container types known from the manufacturer of the second evaluation unit.
  • the specific training data set can be the Error images, the error markings and the error-free container of the container types known to the operator of the beverage processing plant.
  • the training of the second evaluation unit takes place with a lower priority than the detection of the containers with the inspection unit and / or the inspection of the camera images with the first evaluation unit in order to use unused resources of a computer system during the inspection.
  • the training can take place during the inspection of the containers without the acquisition of the camera images and / or the resources used during the inspection with the first evaluation unit being impaired.
  • the computer system can thus be used particularly effectively.
  • the computer system can be a machine control or a PC.
  • “Lower priority” can mean a processing priority by the computer system.
  • a recognition performance of the second evaluation unit is determined on the basis of a verification data set and, if the recognition performance exceeds a predetermined threshold value, that the camera images are inspected for errors by the second evaluation unit using the image processing method based on artificial intelligence.
  • the first evaluation unit can automatically switch from the inspection to the second evaluation unit.
  • the checking data record can contain further error images, error markings and / or error-free images which are not contained in the specific training data record and / or in the generic training data record. As a result, it can be checked whether the image processing method working on the basis of artificial intelligence also works reliably for containers that were not recorded for the generic or specific training data set.
  • the camera images are inspected by the second evaluation unit instead of the first evaluation unit.
  • the resources can be used particularly efficiently for the second evaluation unit.
  • the camera images are additionally inspected by the first evaluation unit in parallel to the second evaluation unit in order to detect unknown errors with the aid of the conventionally operating image processing method for the image processing method based on artificial intelligence.
  • errors still unknown for the image processing method working on the basis of artificial intelligence can be meant errors that are not considered error images and / or for the specific training data set. the assigned error markings have been classified and compiled.
  • the evaluation sensitivity of the first evaluation unit can be reduced or set to standard parameters in order to prevent false rejections.
  • Those of the camera images with containers with the unknown errors can be classified as further error images and the unknown errors can be assigned to the further error images as further error markings, whereby the further error images and the further error markings can be compiled as a further specific training data set, and with The second evaluation unit can be trained with the image processing method based on artificial intelligence on the basis of the further specific training data set on site.
  • the recognition performance of the second evaluation unit with the image processing method based on artificial intelligence can be increased further. It is conceivable that the parallel inspection of the camera images with the first evaluation unit and the second evaluation unit, the classification of those camera images with the containers with the unknown errors, the compilation of the further specific training data set, and the further training of the image processing method based on artificial intelligence. rens continues iteratively on site, in particular until a further recognition performance determined for the second evaluation unit exceeds a predetermined further threshold value.
  • the invention provides a device for the optical inspection of containers in a beverage processing plant with the features of claim 10 ready for solving the above-mentioned problem.
  • Advantageous embodiments of the invention are mentioned in the subclaims.
  • the device can be designed to carry out the method according to any one of claims 1-9.
  • the device can include the features described above with reference to claims 1-9 individually or in any combination.
  • the classification unit is designed to classify those of the camera images with the faulty containers as faulty images and to assign the faults accordingly as fault markings to the faulty images, and to classify those of the camera images with the containers found to be good as faultless images, a large Number of error images and error-free images based on the conventional image processing method to be provided. This can be done, for example, in the beverage processing system with container types in which the conventional image processing method was set up and thus works particularly reliably. Because the classification unit is designed to compile the error images, the error markings and the error-free images as a specific training data set, the specific training data set for training the second evaluation unit can be provided as automatically as possible, whereby the method is particularly time-efficient and therefore cost-effective.
  • the second evaluation unit can be designed to inspect the camera images for errors using the image processing method based on artificial intelligence.
  • the first evaluation unit can automatically switch to the second evaluation unit in order to inspect the camera images from the second evaluation unit for errors using the image processing method based on artificial intelligence.
  • the first evaluation unit and the second evaluation unit inspect the camera images parallel to one another in order to increase an overall detection rate.
  • the device can be arranged in the beverage processing plant. Consequently, the second evaluation unit is trained with the image processing method based on artificial intelligence on site at the beverage manufacturer. “On site” can therefore mean that the second evaluation unit is trained on site at the beverage manufacturer with the training data set.
  • the device can comprise a computer system with the first evaluation unit and the second evaluation unit.
  • the first evaluation unit and the second evaluation unit can be implemented as a computer program product.
  • the computer system includes the classification unit.
  • the classification unit can also be implemented as a computer program product.
  • the computer system can comprise the signal processor and / or the CPU (Central Processing Unit) and / or the GPU (Graphics Processing Unit) and / or the TPU (Tensor Processing Unit) and / or the VPU (Vision Processing Unit).
  • the computer system comprises the storage unit, which provide one or more data interfaces, the network interface, the display unit and / or the input unit.
  • the inspection unit, the first evaluation unit, the second evaluation unit and / or the classification unit can be connected to one another via a digital data link, in particular around the camera images, the error images, the error markings, the error-free images, the specific training data set and / or the generic training data set transferred to.
  • Figure 1 shows an inventive embodiment of a device for optical
  • FIGS. 2A-2B show an exemplary embodiment according to the invention of a method for the optical inspection of containers in a beverage processing plant as a flow diagram.
  • FIG. 1 an exemplary embodiment according to the invention of a device 1 for the optical inspection of containers 2 in a beverage processing system A is shown as a top view.
  • the device 1 is designed to carry out the method 100 in FIGS. 2A-2B described below.
  • the containers 2 are first transferred with the inlet star 9 to the filler 6 and are filled there with a flowable product.
  • the filler 6 comprises, for example, a carousel with filling elements arranged thereon (not shown here) with which the containers 2 are filled with the flowable product during transport. Subsequently, the containers 2 are transferred to the closer 7 via the intermediate star 10 and see there with a closure, for example with a cork, crown cap or with a screw cap.
  • a closure for example with a cork, crown cap or with a screw cap.
  • the containers 2 are then transferred via the discharge star 11 to the conveyor 3, which transports the containers 2 as a container mass flow to the inspection unit 4.
  • the Transpor teur is designed here as an example of a conveyor belt on which the container 2 are transported upright.
  • the inspection unit 4 arranged thereon comprises the lighting device 42 and the camera 41, with which the containers 2 can be detected in transmitted light.
  • the lighting device 42 has, for example, a diffusing light exit disk which is backlit with several LEDs and which thus forms a luminous image background for the container 2 from the perspective of the camera 41.
  • the containers 2 are then captured as camera images with the camera 41 and forwarded to the computer system 5 as a digital data signal.
  • the containers 2 are detected with a different lighting device in incident light.
  • the computer system 5 with the first evaluation unit 51, the second evaluation unit 52 and with the classification unit 53 can be seen.
  • the computer system 5 comprises, for example, a CPU, a memory unit, an input and output unit and a network interface. Accordingly, the first evaluation unit 51, the second evaluation unit 52 and the classification unit 53 are implemented as a computer program product in the computer system 5.
  • the first evaluation unit 51 is designed to inspect the camera images for errors using a conventional image processing method, for example for the fill level and / or soiling.
  • the classification unit 53 is designed to classify those of the camera images with the defective containers as defect images, to assign the defects as defect markings to the defect images and to classify those of the camera images with the containers as good as defect-free images.
  • the classification unit 53 is designed to compile the error images, the error markings and the error-free images as a specific training data set.
  • the second evaluation unit 52 is designed to carry out an image processing method based on artificial intelligence and to train it on site with the specific training data set.
  • the camera images of the containers 2 are first recorded with the first evaluation unit 51 and classified by means of the classification unit 53 so that the specific training data set can be compiled therefrom.
  • the second evaluation unit 52 is then trained on site in the beverage processing plant A with the specific training data set.
  • the inspection can then alternatively or additionally take place with the aid of the second evaluation unit 52.
  • the second evaluation unit 52 is designed to inspect the camera images for errors using the image processing method based on artificial intelligence.
  • the error-free containers 2 are then fed to further processing steps, for example a palletizer.
  • faulty containers are discharged from the container mass flow using a Wei surface and then recycled or disposed of.
  • the mode of operation of the first evaluation unit 51, the second evaluation unit 52 and the classification unit 53 are described in more detail below with reference to FIGS. 2A-2B.
  • FIGS. 2A-2B show an exemplary embodiment according to the invention of a method 100 for the optical inspection of containers 2 in a beverage processing system A as a flow chart.
  • the method 100 is described merely by way of example with reference to the device 1 for optical inspection described above with reference to FIG. 1.
  • step 101 the containers 2 are transported by the conveyor 3 as a container mass flow. This is done, for example, by means of a conveyor belt or a carousel.
  • the containers 2 are captured as camera images by the inspection unit 4 arranged in the beverage processing system.
  • the containers 2 are illuminated, for example, with the lighting unit 42 and captured in transmitted light with the camera 41 as a camera image.
  • the containers 2 are additionally or alternatively illuminated in incident light and captured as camera images with the camera 41.
  • the camera images are then inspected for errors in step 103 by the first evaluation unit 51 using a conventionally operating image processing method.
  • the “conventionally working image processing method” here means a method without artificial intelligence, for example in that the camera images are evaluated using filter and threshold value operations in order to detect errors, such as an incorrect fill level and / or contamination.
  • an edge filter is used, for example, with which the surface of the liquid product can be filtered out of the camera images.
  • step 104 those of the camera images with the faulty containers 2 are classified as fault images and the faults are assigned to the fault images accordingly as fault markings.
  • a fill level and / or an error indicator can be entered as an error marker in the error images, in particular in their metadata.
  • step 105 the classification unit 53 classifies those of the camera plates with the containers 2 found to be good as error-free images.
  • the error images, the error markings and the error-free images are then compiled as a specific training data set (step 106).
  • steps 104 - 106 are carried out with the classification unit 53, for example.
  • the second evaluation unit 52 is trained on site with the specific training data set using the image processing method that works on the basis of artificial intelligence. For example, a deep neural network of the second evaluation unit is trained with the specific training data set.
  • the second evaluation unit 52 with the image processing method working on the basis of artificial intelligence is already trained beforehand at the manufacturer of the beverage processing system with a generic training data set available there.
  • already known container types and error images can be imported at the manufacturer of the beverage processing system in order to train the image processing method working on the basis of artificial intelligence beforehand and then to continue training with the specific training data set on site in the beverage processing system A.
  • the image processing method which works on the basis of artificial intelligence, is further trained on the specific container types available at the beverage manufacturer.
  • the recognition performance of the second evaluation unit 52 is determined on the basis of a verification data set.
  • the checking data set can comprise further error images, error markings and error-free images which are not present either in the generic training data set or in the specific training data set. Consequently, it can therefore be determined whether the second evaluation unit is working reliably.
  • step 111 the camera images can be inspected for errors by the second evaluation unit 52 using the image processing method based on artificial intelligence. This can be done either as an alternative or in addition to the evaluation with the first evaluation unit 51.
  • the artificial intelligence makes it possible to ensure reliable recognition performance without the need for complex parameterization of the classic image processing methods by an expert.
  • the camera images are additionally inspected by the first evaluation unit 51 parallel to the second evaluation unit 52, still unknown errors can be detected with the aid of the conventionally operating image processing method for the image processing method based on artificial intelligence.
  • the evaluation sensitivity of the first evaluation unit 51 can thereby reduce changed or set to standard parameters to prevent false rejection. It is conceivable that those of the camera images with containers 2 with the unknown errors are classified as further error images and the unknown errors are correspondingly assigned to the further error images as further error markings, the further error images and the further error markings being compiled as a further specific training data set the, and with the further specific training data set on site, the second evaluation unit 52 is trained further with the image processing method working on the basis of artificial intelligence. As a result, the recognition performance of the second evaluation unit 52 with the image processing method based on artificial intelligence can be increased further.
  • the camera images are still inspected for errors by the first evaluation unit 51 according to step 110 using the conventionally operating image processing method.
  • the classification unit 53 can further classify the camera images according to steps 104 and 105 and add further error images, error markings and error-free images to the specific training data set or create a further specific training data set.
  • the second evaluation unit 52 can then be trained further in accordance with step 108 until the recognition performance in step 110 exceeds the predetermined threshold value.
  • the training of the second evaluation unit 52 in step 108 takes place with a lower priority than the detection of the containers with the inspection unit 4 in step 102 and / or the inspection of the camera images with the first evaluation unit 51 in step 103 to use unused resources of a computer system 5 for the inspection.
  • the fact that the camera images with the defective containers 2 are classified as defective images and the defects are assigned to the defective images as defective markings, and that those of the camera images with the containers 2 found to be good are classified as error-free images, can result in a large number of defective images and error-free images can be provided on the basis of the conventional image processing method.
  • This can be done, for example, in the beverage processing system A with container types in which the conventionally operating image processing method has been set up and thus works particularly reliably.
  • a specific training data set is then compiled from the error images, the error markings and the error-free images, and the second evaluation unit 52 is trained on site with the image processing method based on artificial intelligence.
  • the specific training data set can be provided automatically as far as possible, as a result of which the method 100 works in a particularly time-efficient and thus cost-effective manner.

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Abstract

L'invention concerne un procédé (100) d'inspection optique de récipients dans un système de traitement de boissons, les récipients étant transportés sous la forme d'un flux massique de récipients grâce à un transporteur (101) et capturés sous la forme d'images de caméra par une unité d'inspection agencée dans le système de traitement de boissons (102), et les images de caméra étant inspectées pour trouver des défauts par une première unité d'évaluation grâce à une méthode de traitement d'image classique (103), les images de caméra avec des récipients défectueux étant classées en images avec défauts et les défauts étant affectés de manière correspondante aux images avec défauts sous la forme de marquages de défaut (104), les images de caméra avec des récipients considérés comme étant de bonne qualité étant classées en images sans défauts (105), les images avec défauts, les marquages de défaut et les images sans défauts étant compilés sous la forme d'un ensemble de données d'entraînement spécifique (106) et, grâce à l'ensemble de données d'entraînement spécifique, une deuxième unité d'évaluation étant entraînée in situ avec une méthode de traitement d'image à base d'intelligence artificielle (108).
EP21716307.0A 2020-04-24 2021-03-29 Procédé et dispositif d'inspection optique de récipients dans un système de traitement de boissons Pending EP4139839A1 (fr)

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DE102020111255 2020-04-24
DE102021106999.8A DE102021106999A1 (de) 2020-04-24 2021-03-22 Verfahren und Vorrichtung zur optischen Inspektion von Behältern in einer Getränkeverarbeitungsanlage
PCT/EP2021/058070 WO2021213779A1 (fr) 2020-04-24 2021-03-29 Procédé et dispositif d'inspection optique de récipients dans un système de traitement de boissons

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US20230177671A1 (en) 2023-06-08

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