US-20260127868-A1 - CLASSIFICATION MODEL GENERATING SYSTEM, CLASSIFICATION MODEL GENERATING METHOD, AND RECORDING MEDIUM

US20260127868A1US 20260127868 A1US20260127868 A1US 20260127868A1US-20260127868-A1

Abstract

A classification model generating system includes: an obtainer that obtains a good product image group including a plurality of good product images; a defective portion image generator that generates a plurality of defective portion images, based on a seed image group obtained by geometrically transforming a seed image simulating a defective portion, the seed image being an artificially drawn image; a combination processing unit that combines each of the plurality of defective portion images with the good product image group to generate a defective product image group; and a classification model generator that performs classification training by using a part of the good product image group and a part of the defective product image group as a training image group to generate a classification model.

Inventors

Yoshinobu FURUTA
Yoshikazu ODAJIMA

Assignees

PANASONIC INTELLECTUAL PROPERTY MANAGEMENT CO., LTD.

Dates

Publication Date: 20260507
Application Date: 20230822
Priority Date: 20221012

Claims (12)

1 . A classification model generating system comprising: an obtainer that obtains a good product image group including a plurality of good product images; a defective portion image generator that generates a plurality of defective portion images, based on a seed image group obtained by geometrically transforming a seed image, the seed image having been artificially generated by drawing a part simulating a defective portion; a combination processing unit that combines each of the plurality of defective portion images with the good product image group to generate a defective product image group; and a classification model generator that performs classification training by using a part of the good product image group and a part of the defective product image group as a training image group to generate a classification model.
2 . The classification model generating system according to claim 1 , wherein the obtainer includes: an input unit to which the plurality of good product images are input; and a good product image generator that geometrically transforms the plurality of good product images input to the input unit to generate one or more new good product images and obtains, as the good product image group, the plurality of good product images and the one or more new good product images.
3 . The classification model generating system according to claim 1 , wherein the classification model generator includes: a trainer that causes a model to undergo classification training by using the training image group; an evaluator that causes the model to perform an evaluation on at least a part of the good product image group and a part of the defective product image group, excluding the training image group used for the classification training by the model, among the good product image group and the defective product image group; and a determiner that determines whether an evaluation result from the evaluation by the model is an incorrect result, and updates the training image group by adding only a good product image or a defective product image determined as an incorrect result to the training image group, and the trainer causes the model to undergo classification training by using the training image group updated, to generate the classification model.
4 . The classification model generating system according to claim 3 , wherein the evaluation result includes a determination result indicating whether an image input to the model is a good product image or a defective product image and certainty of the determination result.
5 . The classification model generating system according to claim 4 , wherein the determiner determines that the evaluation result is an incorrect result when the determination result is incorrect or when the certainty is less than or equal to a predetermined value.
6 . The classification model generating system according to claim 1 , wherein the combination processing unit includes: a combiner that combines each of the plurality of defective portion images with the good product image group; and an image sorter that determines whether each of images generated by the combiner is appropriate as a defective product image.
7 . The classification model generating system according to claim 6 , wherein the image sorter determines that an image combined is appropriate as the defective product image when an area and luminance of a defective portion that appears in the image combined satisfy predetermined criteria, the image combined being each of the images combined, determines that the image combined is not appropriate as the defective product image when the area or luminance of the defective portion that appears in the image combined does not satisfy the predetermined criteria, and includes, in the defective product image group, only the image determined to be appropriate among the images combined.
8 . The classification model generating system according to claim 1 , wherein the defective portion image generator generates the defective portion image by using a generative adversarial network (GAN) model that has been trained, and the GAN model has been trained using the seed image group.
9 . The classification model generating system according to claim 1 , wherein the seed image includes a background and a defect part simulating a defective portion, and the background and the defect are separable by a single threshold.
10 . The classification model generating system according to claim 9 , wherein the background is filled with a single color.
11 . A classification model generating method comprising: obtaining a good product image group that includes a plurality of good product images; generating a plurality of defective portion images, based on a seed image group obtained by geometrically transforming a seed image simulating a defective portion, the seed image being an artificially drawn image; combining each of the plurality of defective portion images with the good product image group to generate a defective product image group; and performing classification training by using a part of the good product image group and a part of the defective product image group as a training image group to generate a classification model.
12 . A non-transitory computer-readable recording medium having stored thereon a program for causing a computer to execute: obtaining a good product image group that includes a plurality of good product images; generating a plurality of defective portion images, based on a seed image group obtained by geometrically transforming a seed image simulating a defective portion, the seed image being an artificially drawn image; combining each of the plurality of defective portion images with the good product image group to generate a defective product image group; and performing classification training by using a part of the good product image group and a part of the defective product image group as a training image group to generate a classification model.

Description

TECHNICAL FIELD The present disclosure relates to a classification model generating system, a classification model generating method, and a program. BACKGROUND ART It is known that, at the time of performing machine learning, such as deep learning, many images are required as training data to ensure the accuracy of a model after machine learning. However, when machine learning is performed on a model for inspection in manufacturing processes, for example, many good product images are easy to obtain, but many defective product images are often difficult to obtain. This is because the number of defective product images obtained in the inspection process is often small relative to the population parameter, namely, the total number of images subjected to the inspection process. Even if many defective product images can be obtained, defect modes are often imbalanced. Therefore, variations in the defective product image to be used as training data cannot be ensured, and even if machine learning is performed on the model using the obtained defective product images and good product images, it is difficult to ensure inspection accuracy that can withstand practical use, which is problematic. Moreover, even if many defective product images can be obtained, a significant number of man-hours are required for a sorting operation, and when the model is reconstructed due to a change or addition of a defect mode, the sorting operation needs to be repeated, which is problematic in terms of maintenance and management. In contrast, for example, Patent Literature (PTL) 1 discloses a defective product image generating method and the like that can multiply defective product images. According to the method disclosed in PTL 1, a defective product image similar to the real thing can be generated from a good product image and a partial image including a defective portion. CITATION LIST Patent Literature [PTL 1] Japanese Unexamined Patent Application Publication No. 2021-135903 Non Patent Literature [NPL 1] Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger, Hayit Greenspan, “GAN-based Synthetic Medical Image Augmentation for Increased CNN Performance in Liver Lesion Classification”, Neurocomputing, Vol. 321, 10 Dec. 2018, pp. 321-331. SUMMARY OF INVENTION Technical Problem However, in the technique disclosed in PTL 1, defective product images are multiplied after a database of defective portions is created, necessitating the collection and sorting of defective product images that include defective portions. The present disclosure has been made in view of the circumstances described above, and an object of the present disclosure is to provide a classification model generating system and the like that can generate, without collecting and sorting defective product images, a classification model capable of classifying between a good product image and a defective product image. Solution to Problem In order to solve the above problem, a classification model generating system according to one embodiment of the present disclosure includes: an obtainer that obtains a good product image group including a plurality of good product images; a defective portion image generator that generates a plurality of defective portion images, based on a seed image group obtained by geometrically transforming a seed image, the seed image having been artificially generated by drawing a part simulating a defective portion; a combination processing unit that combines each of the plurality of defective portion images with the good product image group to generate a defective product image group; and a classification model generator that performs classification training by using a part of the good product image group and a part of the defective product image group as a training image group to generate a classification model. Note that the above general or specific aspect may be implemented by a device, method, system, integrated circuit, computer program, or recording medium such as a computer-readable compact disc read-only memory (CD-ROM), or by any combination of the device, method, system, integrated circuit, computer program, and recording medium. Advantageous Effects of Invention According to the classification model generating system and the like of the present disclosure, it is possible to generate, without collecting and sorting defective product images, a classification model capable of classifying between a good product image and a defective product image. BRIEF DESCRIPTION OF DRAWINGS FIG. 1 is a diagram illustrating an example of a configuration of a classification model generating system according to an embodiment. FIG. 2A is a block diagram illustrating an example of the detailed configuration of the image generating device according to the embodiment. FIG. 2B is a block diagram illustrating an example of the detailed configuration of a pre-processing unit illustrated in FIG. 2A. FIG. 3A is a diagram conceptually illustrating