EP-4742062-A1 - TRAINING IMAGE STORAGE DEVICE AND PRODUCT RETRIEVAL DEVICE

EP4742062A1EP 4742062 A1EP4742062 A1EP 4742062A1EP-4742062-A1

Abstract

A training image storage device that stores training images for learning of an image related to an article, the training image storage device includes: an imaging unit configured to capture an image of the article; a determination unit configured to determine whether a captured image of the article is an outlier image; and a storage unit configured to store at least a part of the captured images as a training image. The determination unit determines whether the captured image is the outlier image based on a trained model related to the article and features of the captured image. The storage unit stores, as the training image, the captured image that is determined not to be the outlier image among a plurality of the captured images.

Inventors

TATEISHI, Keita
NONOHARA, Yasunari
IWAKAWA, KEN

Assignees

Ishida Co., Ltd.

Dates

Publication Date: 20260513
Application Date: 20251105

Claims (4)

A training image storage device configured to store training images for learning of an image related to an article, the training image storage device comprising: an imaging unit configured to capture an image of the article; and a determination unit configured to: determine whether a captured image of the article is an outlier image, and determine whether the captured image is the outlier image based on a trained model related to the article and features of the captured image; wherein the training image storage device further comprises a storage unit configured to store, as a training image, the captured image that is determined not to be an outlier image.
The training image storage device according to claim 1, wherein the determination unit is further configured to: determine that the captured image is the outlier image when an outlier score calculated based on the trained model and the features of the captured image is equal to or greater than a predetermined outlier determination threshold, and calculate the outlier score to be smaller as a capturing time of the captured image is more recent.
The training image storage device according to claim 1 or 2, wherein the determination unit is further configured to determine that the captured image is the outlier image based on a result of a comparison between an outlier score calculated based on the trained model and the features of the captured image, and a predetermined outlier determination threshold, and wherein the outlier determination threshold is a value of the outlier score which results in a predetermined ratio of the captured images among the captured images being determined as outliers.
A product retrieval device comprising: the training image storage device according to any one of claims 1 to 3; an imaging unit configured to capture an image of a product; a product information storage unit configured to store product information related to a type of the product; and a retrieval unit configured to retrieve the product information, wherein the retrieval unit is further configured to identify the type of the product by applying features of a captured image of the product to the trained model that has been trained using the training images, and retrieve the product information corresponding to the identified type of the product.

Description

TECHNICAL FIELD The present disclosure relates to a training image storage device and a product retrieval device. BACKGROUND Japanese Patent Publication No. 7368834 describes a technique in which, in an article processing apparatus, when a new article is conveyed by a conveyance unit and when characteristics of an article change such that a pre-configured trained model becomes difficult to handle the article, teacher data related to the article is automatically collected by storing an image of the article in association with article information. When automatically collecting training images (for example, teacher data) related to articles as in the related art described above, it is desirable to remove outlier images containing noise from a large number of images including an article. However, when there are a large number of images including articles, it is not easy for an operator to determine whether an image is an outlier image. The present disclosure provides a training image storage device and a product retrieval device that are capable of storing, as training images, captured images obtained by automatically removing outlier images including noise from a plurality of captured images. SUMMARY (1) A training image storage device according to an aspect of the present disclosure is a training image storage device that stores training images for learning of an image related to an article, the training image storage device includes: an imaging unit configured to capture an image of the article; a determination unit configured to determine whether a captured image of the article is an outlier image; and a storage unit configured to store at least a part of the captured images as a training image. The determination unit determines whether the captured image is the outlier image based on a pre-configured trained model related to the article and features of the captured image. The storage unit stores, as the training image, the captured image that is determined not to be the outlier image among a plurality of the captured images. In the training image storage device according to the aspect of the present disclosure, the determination unit determines whether the captured image is an outlier image based on the trained model related to the article and the features of the captured image. The storage unit stores, as the training image, the captured image that is determined not to be an outlier image among a plurality of the captured images. Thus, for example, each time the article is imaged by the imaging unit, the captured image determined not to be an outlier image is automatically and sequentially stored as the training image. Therefore, according to the training image storage device of the aspect of the present disclosure, captured images obtained by automatically removing outlier images including noise from a plurality of captured images can be stored as training images.(2) In the above-described (1), the determination unit may determine that the captured image is the outlier image when an outlier score calculated based on the trained model and the features of the captured image is equal to or greater than a predetermined outlier determination threshold. The determination unit may calculate the outlier score to be smaller as a capturing time of the captured image is more recent. In this case, the more recent the capturing time of the captured image, the less likely the captured image is to be determined to be an outlier image. This can suppress erroneously determining a captured image that does not actually include noise as an outlier image when only a part of the article has been changed to the latest specification, for example.(3) In the above-described (1) or (2), the determination unit may determine that the captured image is the outlier image based on a result of a comparison between an outlier score calculated based on the trained model and the features of the captured image, and a predetermined outlier determination threshold. The outlier determination threshold may be a value of the outlier score which results in a predetermined ratio of the captured images among the captured images being determined as outliers. In this case, for example, when an outlier occurrence rate at which a plurality of captured images include noise is known in advance, the outlier determination threshold can be automatically set.(4) A product retrieval device according to another aspect of the present disclosure includes: the training image storage device according to the above-described (1) or (2); an imaging unit configured to capture an image of a product; a product information storage unit configured to store product information related to a type of the product; and a retrieval unit configured to retrieve the product information. The retrieval unit may identify the type of the product by applying features of a captured image of the product to the trained model that has been trained using the training images, and retrieve the