EP-4736111-A1 - METHOD AND DEVICE FOR INSPECTING CONTAINERS IN AT LEAST TWO DIFFERENT OBSERVATION DIRECTIONS WITH A VIEW TO CLASSIFYING THE CONTAINERS

EP4736111A1EP 4736111 A1EP4736111 A1EP 4736111A1EP-4736111-A1

Abstract

The invention relates to a method for inspecting containers made of transparent or translucent material (2) with a view to classifying a container, the method including a use phase comprising: - acquiring, for each container, at least a first and a second image (Ic) of at least one given portion of a container in two different observation directions and using at least one modality; - providing, as input for a deep learning model (NN), for each container, a record of at least the first and the second image of at least one portion of the container using at least one modality and in two different observation directions; - and the deep learning model analysing, for each container, this record in order to determine a result class, from among a list of classes, to which this container portion belongs.

Inventors

BERNARDI, Gwendal
GOURGEON, Sylvain
GARIN, Jean-François

Assignees

TIAMA

Dates

Publication Date: 20260506
Application Date: 20240628

Claims (17)

[Claim 1] A method of inspecting containers made of transparent or translucent material (2) with a view to classifying a container, the method comprising; a use phase comprising: - the acquisition for each container, of at least a first and a second image (the) of at least one portion of a container according to two different observation directions and according to at least one modality; - providing as input to a deep learning model (NN), for each container, a recording of at least the first and second images of at least one portion of the container according to at least one modality and according to two different observation directions; - and the analysis by the deep learning model, for each container, of this recording to determine the membership of this portion of container, to a result class among a list of classes, a multi-view data fusion being implemented before the determination of the membership to a class by analysis, and the determination of the membership to a class takes into account elements coming from each image, jointly, and in which when a defect is observed according to different observation directions, it is considered that it is the same defect.
[Claim 2] The method of claim 1 wherein the method comprises: - the provision of an inspection system for acquiring images according to at least a first modality and a second modality; - the acquisition for each container, of a recording of at least one portion of a container comprising at least two images according to at least two different observation directions according to the first modality and at least one image according to the second modality.
[Claim 3] Method according to one of the preceding claims, according to which the method comprises providing an inspection system configured to acquire images of the same portion of a container according to at least two different observation directions and according to at least one modality taken from the list of the following modalities: absorption, birefringence, refraction, reflection, infrared radiation.
[Claim 4] Method according to one of the preceding claims, according to which each portion of container is classified according to at least one class taken from a list of classes comprising at least one class of absence of defect in the portion and one class of presence of defect in the portion.
[Claim 5] Method according to one of the preceding claims, according to which each portion of container is classified according to at least one class taken from a list of classes comprising at least one class comprising the presence in the portion of at least defects such as, in particular, trapezium, inclusion, bubble.
[Claim 6] Method according to one of the preceding claims, according to which each portion of container is classified according to at least one class taken from a list of classes comprising at least one class comprising the presence in the portion of at least one singularity such as a marking, a mold seal, a notch, a thread, an impression, a stitch, a handle, a counter ring.
[Claim 7] Method according to one of the preceding claims, according to which the method comprises the acquisition, for each container having a central axis, of at least four images of at least one same portion of a container according to four different observation directions and according to at least the first modality, the observation directions being distributed around the central axis two by two according to an azimuth angle of at least 45°.
[Claim 8] Method according to one of the preceding claims, in which at least one sorting characteristic is compared with a rejection criterion, the sorting characteristic and the rejection criterion being dependent on the membership class to decide whether or not the container is compliant, the sorting characteristic being calculated on at least one image of the container according to a modality.
[Claim 9] Method according to one of the preceding claims, in which a step is implemented of taking into account at least one identified defect in order to deduce therefrom adjustment information for at least one control parameter of a container manufacturing installation.
[Claim 10] Method according to one of the preceding claims, wherein: -the deep learning model associates a confidence score with the ranking of containers that are part of an inspected production; - the classification of containers is taken into account only when the confidence score exceeds a confidence threshold for; - count defects by defect class; -and/or decide to reject the container; -and/or trigger an alarm for the presence of at least one critical defect in the inspected production.
[Claim 11] A method of training a deep learning model for inspecting containers made of transparent or translucent material with a view to classifying a container, the method comprising a construction phase comprising: - provision of a learning set comprising recordings each composed of at least a first and a second image of the same portion of container according to two different observation directions and according to at least one modality; - a provision of at least one deep learning model of neurons having been trained on a training set comprising recordings each composed of at least a first and a second image of the same portion of container according to two different observation directions and according to at least one modality, the deep learning model determining at least one membership class (Kj) for said portion from a list of classes, a multi-view data fusion being implemented before the determination of membership in a class by analysis, and the determination of class membership takes into account elements from each image, jointly, and in which when a defect is observed from different observation directions, it is considered to be the same defect.
[Claim 12] Method according to claim 11, according to which the method comprises providing at least one deep learning model having been trained on a training set comprising recordings each composed of at least two images of the same portion of container according to observation directions different by at most 5° during the acquisition of the images.
[Claim 13] Method according to one of claims 11 or 12, according to which the method comprises providing at least one deep learning model having been trained on a training set comprising recordings each composed of images of the same portion of container according to different directions and modalities.
[Claim 14] A method comprising a phase of constructing the method according to any one of claims 11 to 13 to obtain a deep learning model, and a phase of inspecting the method according to any one of claims 1 to 10 using the deep learning model of said construction phase.
[Claim 15] A method according to claim 14, wherein during the construction phase, the image records are ordered according to a determined sequence while during the use phase, the image records are ordered according to a sequence identical to the sequence of the construction phase.
[Claim 16] Device for inspecting containers made of transparent or translucent material leaving a manufacturing or recovery installation with a view to classifying the containers in relation to defects, the device comprising: - an inspection system comprising at least one camera arranged to recover light or radiation coming from at least one portion of a container and configured to acquire images of the same portion of a container according to at least two different observation directions and according to at least one first modality; - an information processing unit connected to the inspection system and comprising a deep learning model, the deep learning model determining at least one membership class for said portion from a list of classes, the deep learning model receiving as input, for each container, a recording of the at least two images of at least one portion of the container according to the first modality and according to two different observation directions, the deep learning model, for each container, analyzing this recording to determine the membership of this portion of container, to a result class from the list of classes, a multi-view data fusion being implemented before determining the membership to a class by analysis, and the determination of the membership to a class takes into account elements originating from each image, jointly, and in which when a defect is observed according to different observation directions, it is considered that it is the same defect.
[Claim 17] A deep learning model obtained by the learning method according to any one of claims 11 to 15.

Description

Description Title of the invention: Method and device for inspecting containers according to at least two different observation directions with a view to classifying the containers. Technical Domain [0001] The present invention relates to the technical field of the inspection of transparent or translucent containers such as, for example, glass bottles, jars or flasks or even plastic preforms or bottles (including returnable containers) for the purpose of their quality control in order to detect and identify possible defects likely to affect these containers. [0002] The subject of the invention finds particularly advantageous applications for analyzing physical characteristics of containers with a view to determining the presence or absence of defects and identifying conforming optical singularities such as decorations, functional reliefs, mold joints or non-conforming optical singularities corresponding to defects, such as for example surface defects, such as folds or crevices, internal defects in the material, such as cracks, inclusions, or bubbles, dimensional defects such as deformations. Previous technique [0003] In the field of manufacturing glass containers, it is known that the manufacturing process comprising melting the glass and then transporting it to forming units is implemented by means of a manufacturing installation comprising a melting furnace, a feeder for supplying molten glass to a forming machine generally of the type designated by IS machine. The containers which have just been formed by the forming machine are placed successively on an output conveyor to form a row of containers. The containers are transported in a row by a conveyor in order to transport them successively to different processing stations. In particular, the formed containers are brought into an annealing furnace, which raises their temperature and then cools them in a controlled manner so that disappear the thermal stresses created by the forming process. Other glass container forming processes are known for table glassware, insulators, syringes and ampoules. For example, there are forming machines such as rotary and sequential presses, and not in parallel aligned sections like IS machines. There are also machines that transform preforms into tubes, especially borosilicate glass, to produce syringes and ampoules dedicated to pharmaceutical products. [0004]It is known to systematically inspect all containers leaving the forming machine, using different inspection equipment, either at an intermediate stage of their forming, or immediately after their forming, when they are passing on the output conveyor, while still hot (what is called hot inspection), or after their annealing in the annealing arch, in inspection equipment (what is called cold inspection). [0005] For example, to detect glaze-type defects in containers moving in a translation direction, patent application WO2021/209704 describes an inspection station comprising at least six imagers (typically 6, 12, 18 or 24) forming images and having an optical axis directed towards the inside of the inspection zone, being mounted so that their optical axes are distributed around the central axis of the containers by choosing their azimuth angles between 0 and 360° relative to the translation direction, so that all the points of the circumference of the edge of the containers are represented in at least one image acquired during the crossing of the inspection zone by the edge of the container. The containers are also illuminated by at least twelve projectors each having a beam direction, tangent to a cylinder centered on the central axis of the container, and the illumination beam directions are distributed in azimuth. Such a device allows for multiple beam directions and multiple observation directions to ensure the detection of glazes that reflect incident light toward the imagers. [0006JII It is also known to systematically inspect all containers leaving the annealing furnace using different inspection equipment, in particular transmission wall inspection systems for which a light source is arranged on one side of the conveyor and at least one camera (typically 2 to 6, 12 or 24) is arranged on the other side to acquire at least one image formed by the light transmitted through the walls of the container. Patent application WO2023/052732 describes an inspection device provided on each side of the conveyor with a series of three cameras opposite which a light panel is arranged. [0007] It is also known from patent EP 1 109 008, a method for analyzing images of containers for cold inspection. A segmentation step detects features in the images and regions around the features. Discriminant parameters of each region corresponding to a feature are calculated and combined with a fuzzy logic method to determine the most probable type of features from a list of features corresponding to possible defects. The conformity of the region is then decided, by applying different crite