US 12,352,727 B2
Acoustic imaging techniques using machine learning
Benoit Lepage, L'Ancienne-Lorette (CA)
Assigned to Evident Canada, Inc., Québec (CA)
Appl. No. 18/041,630
Filed by Evident Canada, Inc., Québec (CA)
PCT Filed Aug. 4, 2021, PCT No. PCT/CA2021/051083
§ 371(c)(1), (2) Date Feb. 14, 2023,
PCT Pub. No. WO2022/032378, PCT Pub. Date Feb. 17, 2022.
Claims priority of provisional application 63/065,850, filed on Aug. 14, 2020.
Prior Publication US 2023/0304968 A1, Sep. 28, 2023
Int. Cl. G01N 29/06 (2006.01); G01N 29/14 (2006.01); G01N 29/26 (2006.01); G01N 29/44 (2006.01)
CPC G01N 29/069 (2013.01) [G01N 29/14 (2013.01); G01N 29/262 (2013.01); G01N 29/4481 (2013.01); G01N 2291/106 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A computerized method of image processing using processing circuitry to apply a previously trained machine learning model in a system for non-destructive testing (NDT) of a material, the method comprising:
acquiring acoustic imaging data of the material, the acoustic imaging data acquired at least in part using an acoustic imaging modality;
generating an acoustic imaging data set corresponding to an acoustic propagation mode;
applying the previously trained machine learning model to the acoustic imaging data set; and
generating an image of the material depicting a probability of a flaw per pixel or voxel based on the application of the previously trained machine learning model.