# US 11,906,441 B2 · Inspection apparatus, control method, and program > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,906,441 B2 - Document ID: US11906441B2 - Status: Granted - Kind: B2 - Application number: 17/615,910 - Filed: 2019-06-03 - Published: 2022-10-06 - Granted: 2024-02-20 - Earliest claimed: 2019-06-03 - Assignees: NEC Corporation - Applicants: NEC Corporation - Inventors: Kyota Higa - Web page: https://patentgrep.com/patent/US11906441B2 ## CPC - G01N 21/8851: {Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges (G01N21/8806 and G01N21/93 - G01N21/95692 take precedence; optical measurement of dimensions G01B11/00; optical scanning G02B26/10; image transformation G06T3/00; computerised image enhancement G06T5/00; image processing per se for flaw detection G06T7/0002)} - G06T 3/00: Geometric image transformations in the plane of the image - G06T 7/00: Image analysis - G06T 7/0008: {checking presence/absence} - G06T 7/168: involving transform domain methods - G06T 7/344: {involving models} - G01N 21/952: Inspecting the exterior surface of cylindrical bodies or wires (G01N21/956 takes precedence) - G01N 2021/8883: {involving the calculation of gauges, generating models} - G01N 2021/8887: {based on image processing techniques} - G06T 2207/10016: Video; Image sequence - G06T 2207/10024: Color image - G06T 2207/10028: Range image; Depth image; 3D point clouds - G06T 2207/10032: Satellite or aerial image; Remote sensing - G06T 2207/10048: Infrared image - G06T 2207/20061: Hough transform - G06T 2207/20076: Probabilistic image processing - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract An inspection apparatus (100) detects an inspection object (90) from first image data (10) in which the inspection object (90) is included. The inspection apparatus (100) generates second image data (20) by performing a geometric transform on the first image data (10) in such a way that a view of the detected inspection object (90) becomes a view satisfying a predetermined reference. In an inference phase, the inspection apparatus (100) detects, by using an identification model for detecting an abnormality of the inspection object (90), an abnormality of the inspection object (90) included in the second image data (20). Further, in a learning phase, the inspection apparatus (100) learns, by using the second image data (20), an identification model for detecting an abnormality of the inspection object (90). ## Claims ### Claim 1 An inspection apparatus comprising: a memory storing instructions; and a processor configured to execute the instructions to: detect an inspection object from first image data in which the inspection object is included; generate second image data by performing a geometric transform on the first image data in such a way that a view of the detected inspection object satisfies a predetermined reference; and detect, by using an identification model, an abnormality of the inspection object included in the second image data, wherein the identification model is learned by using image data in which learning inspection objects having views satisfying the predetermined reference are included, and in the geometric transform, a degree of divergence between a second parameter representing a second view of the inspection object on the second image data and the reference becomes smaller than a degree of divergence between a first parameter representing a first view of the inspection object on the first image data and the reference. ### Claim 2 (depends on 1) The inspection apparatus according to claim 1, wherein the second image data is generated by computing the first parameter representing the first view of the inspection object on the first image data; acquiring a reference parameter representing the predetermined reference; and determining, based on the first parameter and the reference parameter, the geometric transform to apply to the first image data in such a way that the degree of divergence between the second parameter representing the second view of the inspection object on the second image data and the reference parameter is reduced. ### Claim 3 (depends on 2) The inspection apparatus according to claim 2, wherein the first parameter includes any one of a position where a line representing the inspection object and any one edge of the first image data intersect each other, a size of an angle formed by the line representing the inspection object and the any one edge of the first image data, a length of a normal line drawn from an origin to the line representing the inspection object, and an angle of the normal line. ### Claim 4 (depends on 1) The inspection apparatus according to claim 1, wherein the inspection object is detected from the first image data by using teaching data in which an image area representing the inspection object on the first image data is specified. ### Claim 5 (depends on 1) The inspection apparatus according to claim 1, wherein the processor is configured to execute the instructions to further: display an image area representing the inspection object detected from the first image data; and accept an input for correcting the image area representing the inspection object. ### Claim 6 (depends on 1) The inspection apparatus according to claim 1, wherein the processor is configured to execute the instructions to further: determine an area on the first image data equivalent to an abnormal part of the inspection object detected from the second image data and display a predetermined display superimposed on the determined area. ### Claim 7 (depends on 1) The inspection apparatus according to claim 1, wherein the geometric transform uses the second parameter as the reference. ### Claim 8 An inspection apparatus comprising: a memory configured to store instructions; and a processor configured to execute the instructions including: detect an inspection object from first image data in which the inspection object is included; generate second image data by performing a geometric transform on the first image data in such a way that a view of the detected inspection object satisfies a predetermined reference; and learn, by using the second image data, an identification model for detecting an abnormality of the inspection object, wherein in the geometric transform, a degree of divergence between a second parameter representing a second view of the inspection object on the second image data and the reference becomes smaller than a degree of divergence between a first parameter representing a first view of the inspection object on the first image data and the reference. ### Claim 9 (depends on 8) The inspection apparatus according to claim 8, wherein the second image data is generated by computing the first parameter representing the first view of the inspection object on the first image data; acquiring a reference parameter representing the predetermined reference; and determining, based on the first parameter and the reference parameter, the geometric transform to be applied to apply to the first image data in such a way that the degree of divergence between the second parameter representing the second view of the inspection object on the second image data and the reference parameter is reduced. ### Claim 10 (depends on 9) The inspection apparatus according to claim 9, wherein the first parameter includes any one of a position where a line representing the inspection object and any one edge of the first image data intersect each other, a size of an angle formed by the line representing the inspection object and the any one edge of the first image data, a length of a normal line drawn from an origin to the line representing the inspection object, and an angle of the normal line. ### Claim 11 (depends on 8) The inspection apparatus according to claim 8, wherein the inspection object is detected from the first image data by using teaching data in which an image area representing the inspection object on the first image data is specified. ### Claim 12 (depends on 8) The inspection apparatus according to claim 8, wherein the processor is configured to execute the instructions to further: display indicating an image area representing the inspection object detected from the first image data; and accept an input for correcting the image area representing the inspection object. ### Claim 13 (depends on 8) The inspection apparatus according to claim 8, wherein the processor is configured to execute the instructions to further: determine an area on the first image data equivalent to an abnormal part of the inspection object detected from the second image data and display a predetermined display superimposed on the determined area. ### Claim 14 A control method comprising: detecting, by a processor, an inspection object from first image data in which the inspection object is included; generating, by the processor, second image data by performing a geometric transform on the first image data in such a way that a view of the detected inspection object satisfies a predetermined reference; and detecting, by the processor using an identification model, an abnormality of the inspection object included in the second image data, wherein the identification model is learned by using image data in which learning inspection objects having views satisfying the predetermined reference are included, and in the geometric transform, a degree of divergence between a second parameter representing a second view of the inspection object on the second image data and the reference becomes smaller than a degree of divergence between a first parameter representing a first view of the inspection object on the first image data and the reference. ### Claim 15 (depends on 14) The control method according to claim 14, wherein further comprising: generating the second image data comprises: computing the first parameter representing the first view of the inspection object on the first image data; acquiring a reference parameter representing the predetermined reference; and determining, based on the first parameter and the reference parameter, the geometric transform to apply to the first image data in such a way that the degree of divergence between the second parameter representing the second view of the inspection object on the second image data and the reference parameter is reduced. ### Claim 16 (depends on 15) The control method according to claim 15, wherein the first parameter includes any one of a position where a line representing the inspection object and any one edge of the first image data intersect each other, a size of an angle formed by the line representing the inspection object and the any one edge of the first image data, a length of a normal line drawn from an origin to the line representing the inspection object, and an angle of the normal line. ### Claim 17 (depends on 14) The control method according to claim 14, wherein the inspection object is detected from the first image data by using teaching data in which an image area representing the inspection object on the first image data is specified. ### Claim 18 (depends on 14) The control method according to claim 14, further comprising: displaying, by the processor, an image area representing the inspection object detected from the first image data; and accepting, by the processor, an input for correcting the image area representing the inspection object. ### Claim 19 (depends on 14) The control method according to claim 14, further comprising: determining, by the processor, an area on the first image data equivalent to an abnormal part of the inspection object detected from the second image data; and displaying, by the processor, a predetermined display superimposed on the determined area. ### Claim 20 (depends on 14) A non-transitory computer readable medium storing a program executable by a computer to perform the control method according to claim 14.