ABNORMALITY DETECTION DEVICE

• US 20200134384A1
• Filed: 09/14/2017
• Published: 04/30/2020
• Est. Priority Date: 09/14/2017
• Status: Abandoned Application

• Current Assignee: Mitsubishi Electric Corporation
• Original Assignee: Mitsubishi Electric Corporation
• Inventors: HINO, Momoyo, WANG, Mengxiong, SUGIMOTO, Kazuo, MISHIMA, Hidetoshi
• Application Number:
  US16/626,428
• Publication Number:
  US 20200134384A1
• Time in Patent Office: Days
• CPC Class Codes: G01N 21/88 Investigating the presence of flaws or contamination
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30132 Masonry; Concrete
• CPC Class Codes: G06T 2207/30184 Infrastructure
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06T 7/001 using an image reference approach
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 using neural networks

Abstract

The learning model building unit (2) builds a learning model for a convolution neural network, by extracting characteristics of abnormality included in a sample image from the convolution neural network using a kernel having a shape corresponding to the shape of the abnormality included in the sample image and by learning the extracted characteristics.

Claims

1. An abnormality detection device comprising:

processing circuitry

to use, as learning data for a convolution neural network to output a classification result of abnormality, image data indicating a sample image including abnormality, thereby building a learning model for the convolution neural network; and

to give image data indicating an image of an abnormality detection object to the convolution neural network for which the learning model has been built, thereby acquiring the classification result of the abnormality output from the convolution neural network,

wherein the processing circuitry extracts a characteristic of the abnormality included in the sample image from the convolution neural network by using a kernel having a shape corresponding to a shape of the abnormality included in the sample image and learns the extracted characteristic, thereby adjusting the learning model for the convolution neural network, and

the processing circuitry extracts the characteristic of the abnormality using the kernel having a rectangular shape in a case where the abnormality included in the sample image has a linear shape, and extracts the characteristic of the abnormality using the kernel having a square shape in a case where the abnormality included in the sample image has a plane shape.

3. The abnormality detection device according to claim 1, wherein

the processing circuitry modifies the sample image, and

the processing circuitry uses both the image data indicating the sample image and image data indicating the sample image having been modified, as the learning data.

4. The abnormality detection device according to claim 1, wherein the processing circuitry displays the classification result of the abnormality acquired and the image of the abnormality detection object.

5. The abnormality detection device according to claim 4, wherein the processing circuitry accepts correction to the classification result of the abnormality displayed.

2. (canceled)