Patent application EP20180077A (EP), filed 2020-06-15
Publication number: EP3754602A1
Title: DETECTION OF DEVIATIONS IN PACKAGING CONTAINERS FOR LIQUID FOOD
Abstract: A monitoring system implements a method for versatile and efficient detection and grading of deviations in packaging containers for liquid food in a manufacturing plant. The method comprises obtaining (31) image data of a packaging container, or a starting material for use in producing the packaging container; analyzing (32) the image data for detection of a current deviation; processing (33) the current deviation in relation to a set of basis functions, which is associated with a deviation type of the current deviation, to obtain a current set of weights that represent the current deviation; and determining (35) a current grading of the current deviation based on the current set of weights. The set of basis function may be pre-computed based on reproductions of packaging containers or starting material comprising different magnitudes of the deviation type.
IPC codes: G06T   7/    00            A I
G06V  20/    00            A I
G06V  10/    77            A I
Claims: number: 1
text: A method of detecting deviations in packaging containers (401) for liquid food in a manufacturing plant, said method comprising:
number: 2
text: The method of claim 1, further comprising: mapping (34) the current set of weights ([W]c) to a grading database (44) that relates combinations of weights to gradings for the deviation type of the current deviation, wherein the current grading (Gc) of the current deviation is determined based on said mapping.
number: 3
text: The method of claim 1 or 2, wherein said processing further comprises : processing the current deviation for determination of the deviation type.
number: 4
text: The method of any preceding claim, further comprising: obtaining the set of basis functions ([BF]) from a basis function database (43) as a function of the deviation type.
number: 5
text: The method of any preceding claim, further comprising: defining a current feature vector representing the current deviation; and computing the current set of weights ([W]c) as a function of the current feature vector and the set of basis functions ([BF)].
number: 6
text: The method of claim 5, wherein said computing the current set of weights ([W]c) comprises: determining projections of the current feature vector onto the set of basis functions ([BF]); and determining the current set of weights ([W]c) based on scalar values of the projections.
number: 7
text: The method of any preceding claim, wherein the basis functions are linearly independent and/or mutually orthogonal.
number: 8
text: The method of any preceding claim, wherein the basis functions correspond to principal components given by Principal Component Analysis, PCA.
number: 9
text: The method of any preceding claim, further comprising: determining a time stamp for the current deviation; determining, based on the time stamp, associated production parameters of the manufacturing plant, and correlating the time stamp, the current grading (Gc) and the deviation type with the production parameters.
number: 10
text: The method of claim 9, further comprising: communicating control instructions to a machine (400) in the manufacturing plant comprising modified production parameters according to the current grading (Gc) and/or the deviation type.
number: 11
text: The method of any preceding claim, further comprising: causing an alert notification as a function of the current grading (Gc).
number: 12
text: The method of any preceding claim, wherein the deviation type comprises any of: wrinkles in a material of the packaging containers (401) or in the starting material (401'), unsealed flaps of the packaging containers (401), torn or cloudy holes in the packaging containers (401) or the starting material (401'), dents or bumps in the packaging containers (401), delamination of the packaging containers (401) or in the starting material (401'), and flawed patterning and/or coloring and/or holographic or metalized film of a surface of the packaging containers (401) or the starting material (401').
number: 13
text: A method for generating a set of basis functions ([BF]) associated with a deviation type for use in the method of any one of claims 1-12, said method comprising:
number: 14
text: A computer readable medium comprising computer instructions which, when executed by a processor (80), causes the processor (80) to perform the method according to any one of claims 1-13.
number: 15
text: A system for detecting deviations in packaging containers (401) for liquid food produced in a manufacturing plant, said system comprising a processor (80) configured to perform the method according to any one of claims 1-13.
Office action: search_report, dated 2020-12-23
Rejection reasons: inventive_step_epc_56
Prior art cited: ref_id: [IAY]     - LE TAN-TOAN ET AL: "Inspection of specular surfaces using optimized M-channel wavelets", ICASSP, IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING - PROCEEDINGS 1999 IEEE, IEEE, 26 May 2013 (2013-05-26), pages 2405 - 2409, XP032509323, ISSN: 1520-6149, ISBN: 978-0-7803-5041-0, [retrieved on 20131018], DOI: 10.1109/ICASSP.2013.6638086
ref_type: npl
severity: background
ref_id: - LUIZ A O MARTINS ET AL: "Automatic detection of surface defects on rolled steel using Computer Vision and Artificial Neural Networks", IECON 2010 - 36TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY, IEEE, PISCATAWAY, NJ, USA, 7 November 2010 (2010-11-07), pages 1081 - 1086, XP031840573, ISBN: 978-1-4244-5225-5, DOI: 10.1109/IECON.2010.5675519
ref_type: npl
severity: background
ref_id: - LE TAN-TOAN ET AL, "Inspection of specular surfaces using optimized M-channel wavelets", ICASSP, IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING - PROCEEDINGS 1999 IEEE, IEEE, doi:10.1109/ICASSP.2013.6638086, ISSN 1520-6149, ISBN 978-0-7803-5041-0, (20130526), pages 2405 - 2409, (20131018), XP032509323 [I] 1-7,11-15 * the whole document * [A] 9,10 [Y] 8
ref_type: npl
severity: obviousness
ref_id: - LUIZ A O MARTINS ET AL, "Automatic detection of surface defects on rolled steel using Computer Vision and Artificial Neural Networks", IECON 2010 - 36TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY, IEEE, PISCATAWAY, NJ, USA, (20101107), doi:10.1109/IECON.2010.5675519, ISBN 978-1-4244-5225-5, pages 1081 - 1086, XP031840573 [Y] 8 * abstract * * page  1083, column  1, paragraph  l * * page 1084, column 1, section B *
ref_type: npl
severity: obviousness
Final disposition: pending (2020-12-23)