US 11,727,052 B2
Inspection systems and methods including image retrieval module
Xiao Bian, Santa Clara, CA (US); Bernard Patrick Bewlay, Niskayuna, NY (US); Colin James Parris, Brookfield, CT (US); Feng Xue, Clifton Park, NY (US); Shaopeng Liu, Clifton Park, NY (US); Arpit Jain, Dublin, CA (US); and Shourya Sarcar, Niskayuna, NY (US)
Assigned to General Electric Company, Schenectady, NY (US)
Filed by General Electric Company, Schenectady, NY (US)
Filed on Sep. 3, 2020, as Appl. No. 17/11,909.
Prior Publication US 2022/0067082 A1, Mar. 3, 2022
Int. Cl. G06V 10/25 (2022.01); G06F 16/583 (2019.01); G06F 16/51 (2019.01); G06F 16/901 (2019.01); G06N 3/045 (2023.01)
CPC G06F 16/583 (2019.01) [G06F 16/51 (2019.01); G06F 16/9014 (2019.01); G06N 3/045 (2023.01); G06V 10/25 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method of inspecting a component using an image inspection controller that includes a processor communicatively coupled to a memory and configured to operate in accordance with an image retrieval module, said method comprising:
storing, using the processor, at least one inspection image file in the memory;
identifying, using the processor, a region of interest in the at least one inspection image file;
determining, using the processor, at least one foreground feature vector associated with the region of interest in the at least one inspection image file;
accessing, using the processor, a database storing a plurality of image files;
determining, using the processor, a plurality of feature vectors associated with the plurality of image files, wherein each image file of the plurality of image files is associated with at least one feature vector of the plurality of feature vectors, and wherein the image retrieval module includes at least one convolutional neural network configured to learn from the determination of the plurality of feature vectors and increase an accuracy of the image retrieval module in classifying the plurality of image files;
determining, using the processor, at least one hash code for each image file of the plurality of image files based on the plurality of feature vectors;
classifying, using the processor, a subset of the plurality of image files as relevant based on hash codes of the plurality of image files;
sorting, using the processor, the subset of the plurality of image files based on the plurality of feature vectors; and
generating, using the processor, search results based on the sorted subset of the plurality of image files.