Information processing device, determination method, and storage medium

Patent metadata

Field	Value
Publication number	US-2024161267-A1
Application number	US-202218552965-A
Country	US
Kind code	A1
Filing date	Jan 19, 2022
Priority date	Apr 2, 2021
Publication date	May 16, 2024
Grant date	—

Abstract

This invention carries out highly accurate determination even on an image which is likely to cause erroneous determination. An information processing device (1) includes: a classifying section (105) that obtains an output value given in response to inputting an inspection image into a classification model generated by carrying out learning so that distances, in a feature space, between feature quantities extracted from an image group not having a noise become small; and a determining section (102) that applies, in accordance with the output value, a method for the image group not having a noise or a method for an image group having a noise to determine the presence or absence of a defect.

First claim

1 . An information processing device comprising:
an obtaining section that obtains an output value given in response to inputting a target image into a classification model generated by carrying out learning so that distances between feature quantities extracted from a first image group having a common feature become small when the feature quantities are embedded in a feature space; and a determining section that applies, on a basis of the output value, a first method for the first image group or a second method for a second image group, which is constituted by an image not belonging to the first image group, to determine a given determination matter relating to the target image.

2 . The information processing device according to claim 1 , wherein:
the first image group is an image group for which determination on a basis of an output value given in response to inputting the target image into a learned model generated by machine learning is effective; the first method includes at least a process of determining the determination matter with use of the learned model; and the second method includes at least a process of determining the determination matter through numerical analysis of pixel values in the target image.

3 . The information processing device according to claim 1 , wherein:
the first image group is an image group for which determination on a basis of an output value given in response to inputting the target image into a learned model generated by machine learning is effective; the first method is a method of determining the determination matter with use of a plurality of methods and then integrating results of the determinations to carry out final determination; the plurality of methods include
a method of determining the determination matter with use of the learned model and
a method of determining the determination matter on a basis of the output value from the classification model; and
the second method is a method of determining the determination matter through numerical analysis of pixel values in the target image.

4 . The information processing device according to claim 1 , wherein:
the given determination matter is presence or absence of an abnormal portion in a target in the target image; an image included in the first image group is an image of a target which does not include a pseudo abnormal portion similar in appearance to the abnormal portion; an image included in the second image group is an image of a target including the pseudo abnormal portion; the output value from the classification model indicates whether the target image belongs to the second image group, the target image is an image belonging to the first image group and including the abnormal portion, or the target image is an image belonging to the first image group and not including the abnormal portion; the first method includes at least a process of determining presence or absence of the abnormal portion in the target on a basis of the output value; and the second method includes at least a process of determining presence or absence of the abnormal portion in the target through numerical analysis of pixel values in the target image.

5 . The information processing device according to claim 1 , wherein:
the first image group is an image group for which determination on a basis of an output value given in response to inputting the target image into a learned model generated by machine learning is effective; the determination section determines the determination matter by a plurality of methods and then synthesizes results of the determinations to determine the determination matter; the plurality of methods include a method of determining the determination matter with use of the learned model and a method of determining the determination matter through numerical analysis of pixel values in the target image; the information processing device further comprises a weight setting section that sets weights on the results of the determinations, the weights being used in integration of the results of the determinations; in a case where the first method is applied, the weight setting section sets a weight on the result of the determination given by the method involving use of the learned model so as to be equal to or heavier than a weight on the result of the determination given by the method carrying out the numerical analysis; and in a case where the second method is applied, the weight setting sections sets a weight on the result of the determination given by the method carrying out the numerical analysis so as to be heavier than a weight on the result of the determination given by the method involving use of the learned model.

6 . The information processing device according to claim 5 , further comprising:
a reliability determining section that carries out, for each of the plurality of methods, a process of determining, on a basis of the target image, a reliability which is an indicator indicating a degree of certainty of the result of the determination, wherein the determining section determines the determination matter with use of the results of the determinations, the reliabilities determined by the reliability determining section, and the weights set by the weight setting section.

7 . A determination method executed by an information processing device, comprising:
an obtaining step of obtaining an output value given in response to inputting a target image into a classification model generated by carrying out learning so that distances between feature quantities extracted from a first image group having a common feature become small when the feature quantities are embedded in a feature space; and a determination step of applying, on a basis of the output value, a first method for the first image group or a second method for a second image group, which is constituted by an image not belonging to the first image group, to determine a given determination matter relating to the target image.

8 . A computer-readable, non-transitory storage medium in which a determination program is stored, the determination program causing a computer to function as an information processing device recited in claim 1 , the determination program causing the computer to function as the obtaining section and the determining section.

Assignees

• Hitachi Zosen Corp

Inventors

• Shinoda Kaoru
• Katayama Takeru
• Abe Masamitsu
• Ioka Ryota
• Wada Takahiro
• Hattori Hiroshi
• Murakami Joichi

Classifications

• G06V10/87

  using selection of the recognition techniques, e.g. of a classifier in a multiple classifier system · CPC title

• G06T7/0004 Primary

  Industrial image inspection · CPC title

• G06V10/42

  Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation · CPC title

• G06T2207/30136

  Metal · CPC title

• G06T7/00 Primary

  Image analysis · CPC title