ABNORMALITY DETECTION DEVICE
First Claim
Patent Images
1. An abnormality detection device comprising:
- processing circuitryto 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, andthe 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.
1 Assignment
0 Petitions
Accused Products
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.
-
Citations
5 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. - View Dependent Claims (3, 4, 5)
-
-
2. (canceled)
Specification