US 12,217,411 B2
Inspection apparatus, unit selection apparatus, inspection method, and computer-readable storage medium storing an inspection program
Masashi Kurita, Kyoto (JP); Sakon Yamamoto, Kyoto (JP); Yuki Hasegawa, Kyoto (JP); Yuki Hanzawa, Kyoto (JP); Shigenori Nagae, Kyoto (JP); and Yutaka Kato, Kyoto (JP)
Assigned to OMRON CORPORATION, Kyoto (JP)
Appl. No. 17/999,715
Filed by OMRON Corporation, Kyoto (JP)
PCT Filed May 28, 2021, PCT No. PCT/JP2021/020336
§ 371(c)(1), (2) Date Nov. 23, 2022,
PCT Pub. No. WO2021/261168, PCT Pub. Date Dec. 30, 2021.
Claims priority of application No. 2020-108201 (JP), filed on Jun. 23, 2020.
Prior Publication US 2023/0222645 A1, Jul. 13, 2023
Int. Cl. G06T 7/00 (2017.01); G06V 10/72 (2022.01); G06V 10/77 (2022.01); G06V 10/82 (2022.01)
CPC G06T 7/0004 (2013.01) [G06V 10/72 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. An inspection apparatus, comprising a processor configured with a program to perform operations comprising:
operation as a data obtainer configured to obtain a target image of a target product to be inspected;
operation as an area extractor configured to extract an attention area from the obtained target image using a first estimation model, the first estimation model being generated by training to extract, as the attention area, an image area having a probability of a defect being comprised therein, based on first training data, the first training data comprising a plurality of first training images of defect-free products obtained in a target inspection environment;
operation as a model computation unit configured to perform a computational process with a second estimation model using the attention area extracted from the target image by the first estimation model,
the second estimation model being generated by training to determine a likelihood of the defect being comprised therein, based on second training data comprising a plurality of second training images of defects,
the computational process with the second estimation model comprising a plurality of projection processes performed on the target image, the plurality of projection processes comprising projecting the target image into different spaces with lower dimensions to generate a plurality of feature maps with different dimensions,
the extracted attention area being integrated into at least one of the plurality of feature maps in the computational process with the second estimation model;
operation as a determiner configured to determine whether the target product has a defect based on a computation result from the second estimation model; and
operation as an output unit configured to output information indicating a result of determining whether the target product has a defect.