JP-7854698-B2 - Anomaly detection device and anomaly detection method

JP7854698B2JP 7854698 B2JP7854698 B2JP 7854698B2JP-7854698-B2

Inventors

徳永旭将

Assignees

国立大学法人九州工業大学

Dates

Publication Date: 20260507
Application Date: 20211122

Claims (6)

An anomaly detection device that uses a neural network to detect an anomaly in an object being inspected based on an inspection image taken of the object being inspected, A defect-adding image generation unit generates multiple defect-adding images from the aforementioned inspection image, each having a defect region of a predetermined size and shape according to the size of the inspection image, the accuracy of anomaly detection, and the required processing speed. An interpolation image generation unit using a trained model that has been trained to output an interpolated image in which the missing region has been interpolated when the aforementioned missing image is input, A reconstruction image generation unit generates a reconstructed image by inputting a plurality of missing image projections generated by the missing image projection unit into the interpolation image generation unit, and synthesizing the plurality of interpolation images output from the interpolation image generation unit. An abnormality determination unit that detects abnormal regions in the inspection image based on the difference between the reconstructed image and the inspection image and performs an abnormality determination of the object to be inspected, An anomaly detection device characterized by comprising:
The anomaly detection device according to claim 1, characterized in that the multiple images with missing data generated by the missing data image generation unit have different locations for the missing data regions.
The anomaly detection device according to claim 2, characterized in that the multiple images with missing data generated by the missing data image generation unit have randomly shifted positions of the missing data regions.
The anomaly detection device according to claim 2, characterized in that the multiple images with missing data generated by the missing data generation unit partially overlap in the positions of the missing data regions.
The abnormality determination unit, A difference image generation unit generates a difference image between the reconstructed image and the inspection image, A region size determination unit that determines that there is an abnormality in the inspection target when the size of the abnormal region in the difference image exceeds a predetermined threshold, An abnormality detection device according to any one of claims 1 to 4, characterized by comprising:
An anomaly detection method that uses a neural network to detect abnormalities in an object under inspection based on an image of the object under inspection, A defect-adding image generation step of generating multiple defect-adding images from the inspection image, each having a defect region of a predetermined size and shape according to the size of the inspection image, the accuracy of anomaly detection, and the required processing speed, An interpolation image generation step in which multiple interpolation images are generated using a trained model that has been trained to output an interpolated image in which the missing region has been interpolated when the missing image is input, A reconstructed image generation step involves synthesizing multiple interpolated images to generate a reconstructed image, An abnormality determination step involves detecting abnormal regions in the inspection image based on the difference between the reconstructed image and the inspection image, and performing an abnormality determination of the object to be inspected. An anomaly detection method characterized by comprising:

Description

Article 30, Paragraph 2 of the Patent Law applies. This was presented using video at the 23rd Workshop on Information-Theoretic Learning Theory (IBIS2020), held online from November 23rd to 26th, 2020. This invention relates to an anomaly detection device and an anomaly detection method for detecting anomalies in an inspection target based on an image. Detecting anomalies in images using deep learning technology requires a large number of training images for supervised learning. However, images containing anomalies are not actively collected. On the other hand, socially, detecting anomalies is more important than detecting normal images, and there is a need for methods to detect anomalies with few examples of anomaly data, or even without any supervised data on anomalies. In recent years, image transformation using Generative Adversarial Networks (GANs) has been actively researched, and a technique called GLCIC has attracted attention for achieving extremely natural interpolation in the problem of interpolating missing regions in images. By using GLCIC and training the model to interpolate intentionally introduced missing regions in images without anomalies, the interpolation accuracy will significantly decrease in regions containing anomalies, making it possible to identify anomaly regions without using examples of anomaly images. Globally and Locally Consistent Image Completion [Iizuka et al., 2017] This is a block diagram showing an example of an anomaly detection device of the present invention.Figure 1 is a block diagram showing the creation of a trained model.This is a conceptual diagram illustrating the flow of anomaly detection using the anomaly detection device of the present invention.These diagrams illustrate image processing. (A) shows the inspection image when there is no abnormality in the subject being inspected, (B) shows the missing area when there is no abnormality in the subject being inspected, (C) shows the interpolated image when there is no abnormality in the subject being inspected, (D) is a magnified view of the part corresponding to the missing area in the interpolated image when there is no abnormality in the subject being inspected, (E) is a magnified view of the part corresponding to the missing area in the inspection image when there is no abnormality in the subject being inspected, (F) shows the inspection image when there is an abnormality in the subject being inspected, (G) shows the missing area when there is an abnormality in the subject being inspected, (H) shows the interpolated image when there is an abnormality in the subject being inspected, (I) is a magnified view of the part corresponding to the missing area in the interpolated image when there is an abnormality in the subject being inspected, and (J) is a magnified view of the part corresponding to the missing area in the inspection image when there is an abnormality in the subject being inspected.The diagrams illustrate image processing, with (A) showing the initial image when there is no abnormality in the subject, (B) showing the reconstructed image when there is no abnormality in the subject, (C) showing the difference image when there is no abnormality in the subject, (D) showing the initial image when there is an abnormality in the subject, (E) showing the reconstructed image when there is an abnormality in the subject, and (F) showing the difference image when there is an abnormality in the subject.This flowchart shows the processing steps for the anomaly detection method of the present invention. Hereinafter, an example of an embodiment of the abnormality detection device according to the present invention will be described with reference to the drawings. Figures 1 to 5 show an anomaly detection device according to the present invention. The anomaly detection device 1 uses a neural network to detect anomalies in an inspection target based on an inspection image 2 taken of the inspection target. It comprises a missing image generation unit 5 that generates multiple missing image 4 having missing regions 3 from the inspection image 2, an interpolation image generation unit 8 that uses a trained model 7 which is trained to output an interpolated image 6 in which the missing regions 3 are interpolated when a missing image 4 is input, a reconstructed image generation unit 10 that generates a reconstructed image 9 by synthesizing multiple interpolated images 6 output from the interpolation image generation unit 8 when multiple missing image 4 generated by the missing image generation unit 5 are input to the interpolation image generation unit 8, and an anomaly determination unit 12 that detects anomaly regions 11 in the inspection image 2 based on the difference between the reconstructed image 9 and the inspection image 2 and performs an anomaly determination of the inspection target. In this embodiment, a smoothing processing unit 13 is provided that performs smoothing processing on the inspection image 2 and th