Abstract: A method for detection of deviations in packaging containers is disclosed, comprising creating a virtual model of a packaging container in a virtual coordinate system (x, y, z), defining a deformation zone on a surface of the virtual model, creating a defined deviation in the deformation zone having a defined geometry and coordinates in the virtual coordinate system (x, y, z) to create a controlled deformation of the virtual model, producing an image rendering of the virtual model with said controlled deformation to generate image features representing a deviation in the packaging container, associating the image features with different categories of deviations, and inputting the image features to a machine learning-based model for subsequent detection of categories of deviations in packaging containers in a packaging machine based on the image features. A system for detection of deviations is also disclosed.

Abstract: A method of defect detection in packaging containers for liquid food is disclosed, where packaging containers are produced in a machine. The method comprises capturing image data of the packaging containers, defining image features in the image data representing defects in the packaging containers, associating the image features with different categories of defects, inputting the image features to a machine learning-based model for subsequent detection of categories of defects in packaging containers based on the image features, determining time stamps for the occurrence of defects in said subsequent detection, determining associated production parameters of the packaging containers in the machine for the occurrence of defects based on the time stamps, and correlating said occurrence and category of the defects with said production parameters. A system for defect detection in packaging containers is also disclosed.

Abstract: A method for detection of deviations in packaging containers is disclosed, comprising creating a virtual model (201) of a packaging container (401) in a virtual coordinate system (x, y, z), defining a deformation zone on a surface of the virtual model (201), creating a defined deviation in the deformation zone having a defined geometry and coordinates in the virtual coordinate system (x, y, z) to create a controlled deformation of the virtual model (201), producing an image rendering of the virtual model (201) with said controlled deformation to generate image features representing a deviation in the packaging container (401), associating the image features with different categories of deviations, and inputting the image features to a machine learning-based model for subsequent detection of categories of deviations in packaging containers (401) in a packaging machine (400) based on the image features. A system for detection of deviations is also disclosed.

Abstract: A method of defect detection in packaging containers for liquid food is disclosed, where packaging containers are produced in a machine. The method comprises capturing image data of the packaging containers, defining image features in the image data representing defects in the packaging containers, associating the image features with different categories of defects, inputting the image features to a machine learning-based model for subsequent detection of categories of defects in packaging containers based on the image features, determining time stamps for the occurrence of defects in said subsequent detection, determining associated production parameters of the packaging containers in the machine for the occurrence of defects based on the time stamps, and correlating said occurrence and category of the defects with said production parameters. A system for defect detection in packaging containers is also disclosed.