# US 2023/0410285 A1 · Abnormality detection system, learning apparatus, abnormality detection program, and learning program > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0410285 A1 - Document ID: US20230410285A1 - Status: Published application - Kind: A1 - Application number: 18/037,817 - Filed: 2021-11-08 - Published: 2023-12-21 - Earliest claimed: 2020-12-25 - Assignees: Konica Minolta, Inc. - Applicants: Konica Minolta, Inc. - Inventors: Kiichi OKUNO; Tomoya OKAZAKI; Takehiko SASHIDA; Koki TACHI - Web page: https://patentgrep.com/patent/US20230410285A1 ## CPC - G06T 7/001: {using an image reference approach} - G06V 10/82: using neural networks - G01N 21/88: Investigating the presence of flaws or contamination - G06V 10/48: by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation - G06V 10/761: {Proximity, similarity or dissimilarity measures} - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - 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) ## Abstract Stable determination accuracy is secured regardless of an image size, in abnormality detection of detecting a visual defect of an object. An abnormality detection system includes an input unit, a feature extractor, an image generator, and a detector. The input unit acquires inspection images of a target object, the inspection images having different image sizes each of which is equal to or more than a predetermined size. The feature extractor is previously learned to extract a feature map from training images including a non-defective image of the target object. The image generator is previously learned to restore the training images from the feature map extracted by the feature extractor. The detector compares the inspection image of the target object, which is an inspection target, the inspection image being input to the input unit, with a corresponding restored image restored from the inspection image by the feature extractor and the image generator. The inspection image has one of the different image sizes each of which is equal to or more than the predetermined size. The detector detects an abnormality of the target object, based on a calculated similarity. ## Claims ### Claim 1 An abnormality detection system for detecting a visual defect of an object, the abnormality detection system comprising: an input unit that acquires inspection images of a target object, the inspection images having different image sizes each of which is equal to or more than a predetermined size; a feature extractor that is previously learned to extract a feature map from training images including a non-defective image of the target object; an image generator that is previously learned to restore the training images from the feature map extracted by the feature extractor; and a detector that detects an abnormality of the target object, based on a similarity calculated by comparing inspection image of the target object which is an inspection target, the inspection image being input to the input unit and having one of the different image sizes each of which is equal to or more than the predetermined size, with a corresponding the restored image restored from the inspection image by the feature extractor and the image generator. ### Claim 2 (depends on 1) The abnormality detection system according to claim 1, wherein the detector is set to detect the abnormality of the target object at a degree of accuracy equal to or more than a certain level, regardless of the image sizes of the inspection image input to the input unit. ### Claim 3 (depends on 1) The abnormality detection system according to claim 1, wherein the feature map extracted by the feature extractor has a size equal to or more than a size of 8 by 8 pixels. ### Claim 4 (depends on 3) The abnormality detection system according to claim 3, wherein on condition that the sizes of the inspection image are indicated by M and the size of the feature map is indicated by N, the feature map extracted by the feature extractor satisfies the following formula (1): N≥M×(½){circumflex over ( )}a Formula (1), where M and N each represent a number of vertical or horizontal pixels, and a represents a number of convolution layers in the feature extractor. ### Claim 5 (depends on 3) The abnormality detection system according to claim 3, wherein the size of the feature map extracted by the feature extractor is proportional to the sizes of the inspection image input to the input unit. ### Claim 6 (depends on 1) The abnormality detection system according to claim 1, wherein the feature extractor extracts the feature map from which spatial information on an image is not lost. ### Claim 7 (depends on 6) The abnormality detection system according to claim 6, wherein the feature extractor does not include a fully connected layer or a global average pooling (GAP) layer. ### Claim 8 (depends on 1) The abnormality detection system according to claim 1, wherein the feature extractor and the image generator each have a structure to be changed in accordance with the sizes of the input inspection image. ### Claim 9 (depends on 1) The abnormality detection system according to claim 1, wherein the inspection image is image of an electronic circuit. ### Claim 10 A learning apparatus for learning a learning model that carries out abnormality detection of detecting a visual defect of an object, the learning model including a feature extractor and an image generator, the learning apparatus comprising: an input unit that acquires training images including a non-defective image of a target object; the feature extractor that extracts a feature map, based on the training images input to the input unit; the image generator that generates restored image by restoring the training images from the feature map extracted by the feature extractor; and a learning unit that updates parameters of the feature extractor and image generator, based on the training images and the restored images, wherein the training images input to the input unit have different image sizes each of which is equal to or more than a predetermined size. ### Claim 11 (depends on 10) The learning apparatus according to claim 10, wherein the feature map extracted by the feature extractor has a size equal to or more than a size of 8 by 8 pixels. ### Claim 12 (depends on 11) The learning apparatus according to claim 11, wherein on condition that the sizes of the training images are each indicated by M and the size of the feature map is indicated by N, the feature map extracted by the feature extractor satisfies the following formula (1): N≥M×(½){circumflex over ( )}a Formula (1), where M and N each represent a number of vertical or horizontal pixels, and a represents a number of convolution layers in the feature extractor. ### Claim 13 (depends on 10) The learning apparatus according to claim 10, wherein the feature extractor extracts the feature map from which spatial information on an image is not lost. ### Claim 14 (depends on 13) The learning apparatus according to claim 13, wherein the feature extractor does not include a fully connected layer or a global average pooling (GAP) layer. ### Claim 15 (depends on 1) An abnormality detection program for causing a computer to function as the abnormality detection system according to claim 1. ### Claim 16 (depends on 10) A learning program for causing a computer to function as the learning apparatus according to claim 10.