JP-7855166-B2 - Data creation device

JP7855166B2JP 7855166 B2JP7855166 B2JP 7855166B2JP-7855166-B2

Inventors

高野洋一

Assignees

株式会社ダイフク

Dates

Publication Date: 20260508
Application Date: 20221019

Claims (2)

A data creation device for creating training data for machine learning, A data input unit that accepts real data of an object obtained from physical space and virtual data obtained from modeling an object in virtual space, A noise information acquisition unit that acquires the difference between the real data and the virtual data for the same subject as noise information that is included in the real data but not in the virtual data, A noise information holding unit that holds multiple pieces of the aforementioned noise information, A noise information creation unit synthesizes multiple noise pieces identified from the multiple noise pieces held by the noise information holding unit to create new noise pieces that include the content of each of the identified noise pieces. The system includes a training data creation unit that synthesizes the noise information with virtual data of a recognition target obtained by modeling the recognition target from a virtual space to create the training data of the recognition target , A data creation device characterized in that the noise information held by the noise information holding unit, which is available for creating training data, is augmented when the new noise information is stored in the noise information holding unit .
The noise information acquisition unit, The data creation apparatus according to claim 1, characterized in that it corrects the real data or the virtual data so as to eliminate any discrepancies in the shape, size, or position of the contour of the object between the real data and the virtual data for the same object, and then calculates the difference.

Description

This invention relates to a data creation device for creating training data for machine learning. In recent years, the practical application of object recognition and speech recognition using machine learning, such as deep learning, has been progressing. To improve the accuracy of object recognition and speech recognition using machine learning, a large amount of training data is required; therefore, data augmentation is used to increase the amount of training data. Patent Document 1 discloses an example of a data augmentation system. Japanese Patent Publication No. 2021-120914 This is a block diagram showing the schematic configuration of the data creation device 1 of this embodiment.This is a flowchart illustrating the noise information acquisition process.This is an explanatory diagram regarding the operation of acquiring noise information.This is an explanatory diagram regarding the operation of acquiring noise information.This is a flowchart illustrating the process of creating training data.This is an explanatory diagram of the first pattern of method for creating training data.This is an explanatory diagram of the second pattern of method for creating training data.This is an explanatory diagram of the third pattern of method for creating training data.This is a flowchart illustrating the process of creating noise information.This is an explanatory diagram regarding the noise information creation process. One embodiment of the present invention will be described below with reference to the drawings. 1. Configuration of the Data Creation Device Figure 1 is a block diagram showing the schematic configuration of the data creation device 1 according to this embodiment. The data creation device 1 is a device for creating training data for deep learning, and comprises a data input unit 11, a noise information acquisition unit 12, a noise information storage unit 13, a training data creation unit 14, a noise information creation unit 15, a data output unit 16, and a control unit 17. Deep learning is a form of machine learning. Furthermore, the data creation device according to the present invention can be configured to create training data for machine learning that enables speech recognition, as well as training data for machine learning that enables object recognition. However, as an example, the data creation device 1 that creates training data for machine learning that enables object recognition will be described below. The data input unit 11 accepts input of real data obtained by photographing an object (a form of "object" according to the present invention) and virtual data modeled from the object. Real data is data obtained by photographing a real object and represents the appearance of that object. This real data can also be viewed as data obtained from a visual object obtained from physical space. Virtual data, on the other hand, represents the appearance of a model obtained by modeling a real or virtual object. This modeling can be performed, for example, using CAD software. Real data and virtual data may be either two-dimensional or three-dimensional data representing an object. In the following explanation, as an example, real data and virtual data are assumed to be two-dimensional data representing an object, and this data is assumed to be color image data representing the object's appearance (data containing information on multiple pixels to which RGB (red, green, blue) brightness is assigned). Real data, being two-dimensional data, can be obtained, for example, by photographing the object with a digital camera. Virtual data, being two-dimensional data, may be, for example, image data of a 2D model of an object modeled with 2D CAD software, or image data of the appearance of a 3D model of an object modeled with 3D CAD software viewed from one direction. The noise information acquisition unit 12 acquires noise information, which is information specific to real data and not included in virtual data. That is, even with real data and virtual data about the same object, there is information included in the real data but not in the virtual data, and the noise information acquisition unit 12 acquires this information as noise information. Examples of noise information include halation, occlusion, differences in labels attached to objects, and differences in object color. The operation for acquiring noise information (noise information acquisition operation) will be explained in detail later. The noise information storage unit 13 stores noise information acquired by the noise information acquisition unit 12 and noise information newly created by the noise information creation unit 15. As described later, the noise information held by the noise information storage unit 13 can be used to create training data. The training data creation unit 14 creates training data using the noise information held by the noise information storage unit 13. The operation for creating the training data (training data creati