US 12,293,511 B2
Anomaly detection system in the automatic placement of composites during the manufacturing of structural elements
Raúl Alberto Cabañas Contreras, Getafe (ES); and Maria Perez Pintado, Getafe (ES)
Assigned to Airbus Operations S.L.U., Getafe (ES)
Filed by Airbus Operations S.L.U., Getafe (ES)
Filed on Jun. 26, 2023, as Appl. No. 18/341,547.
Claims priority of application No. ES202230572 (ES), filed on Jun. 27, 2022.
Prior Publication US 2023/0419477 A1, Dec. 28, 2023
Int. Cl. H04N 7/18 (2006.01); G06T 7/00 (2017.01); G06V 10/20 (2022.01); G06V 10/764 (2022.01); G06V 20/50 (2022.01); G06V 20/70 (2022.01); H04N 23/50 (2023.01); H04N 23/695 (2023.01)
CPC G06T 7/001 (2013.01) [G06V 10/20 (2022.01); G06V 10/764 (2022.01); G06V 20/50 (2022.01); G06V 20/70 (2022.01); H04N 23/50 (2023.01); H04N 23/695 (2023.01); G06T 2207/20081 (2013.01); G06T 2207/30108 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A system to detect anomalies in an automatic placement of composite materials during a manufacturing of structural elements, comprising the following modules:
an image capture module that can be integrated into an automatic placement machine for composite materials and comprising at least one camera configured to capture images of a surface to be inspected during the manufacture of a structural element comprising the surface, the image capture module being configured to extract data from the images captured by the at least one camera;
an artificial vision module configured to receive the data extracted by the image capture module and to obtain, by means of a computer vision algorithm using the extracted data, information on anomalies detected on the surface; and
a human-machine interface module configured to receive the information of detected anomalies obtained by the artificial vision module and automatically translate the information received into a language understandable to humans;
wherein the artificial vision module is further configured to
identify, using a labeling algorithm for identifying cluster areas, all the areas that the computer vision algorithm detects as anomalies,
calculate a porosity parameter from a total area value of each cluster area and a Euler number, and
compare a given minimum size of areas to be inspected together with the calculated porosity parameter, rule out false positives in the detection of anomalies by the computer vision algorithm.