Anomaly detection method, anomaly detection device, and recording medium
View Patent ↗An anomaly detection method by which a computer performs anomaly detection includes: obtaining first feature data outputted through N (N is an integer not less than 1) convolutional layers of a convolutional neural network configured as an encoder when an image is inputted to the convolutional neural network; obtaining second feature data outputted through M (M is an integer not less than 1, and M≠N) convolutional layers of the convolutional neural network and different in size from the first feature data; and performing anomaly detection on the image by using features indicated by the first feature data and the second feature data that are different in size.
1 . An anomaly detection method by which a computer performs anomaly detection, the method comprising:
obtaining first feature data outputted through N convolutional layers of a convolutional neural network configured as an encoder when an image is inputted to the convolutional neural network, N being an integer not less than 1;
obtaining second feature data outputted through M convolutional layers of the convolutional neural network and different in size from the first feature data, M being an integer not less than 1 and not equal to N; and
performing anomaly detection on the image by using features indicated by the first feature data and the second feature data that are different in size, wherein
at least part of the M convolutional layers is shared with at least part of the N convolutional layers.
2 . The anomaly detection method according to claim 1 , wherein
the performing of the anomaly detection includes:
obtaining first output data outputted from a first decoder including a neural network, by inputting the first feature data to the first decoder;
obtaining second output data outputted from a second decoder including a neural network, by inputting the second feature data to the second decoder; and
generating and outputting an output image indicating a location in which an anomaly in the image is shown, by aggregating the first output data and the second output data.
3 . The anomaly detection method according to claim 2 , wherein
the first decoder and the second decoder are trained to output first output data and second output data that satisfy a predetermined condition, respectively, in response to input of first feature data and second feature data obtained with respect to a normal image in which no anomaly is shown, and
the predetermined condition is that an output image indicating no anomaly is generated by aggregating the first output data and the second output data.
4 . A non-transitory computer-readable recording medium having recorded thereon a program for causing a computer to execute the anomaly detection method according to claim 1 .
5 . An anomaly detection device in which a computer performs anomaly detection, the device comprising:
a processor; and
memory connected to the processor, wherein
using the memory, the processor:
obtains first feature data outputted through N convolutional layers of a convolutional neural network configured as an encoder when an image is inputted to the convolutional neural network, N being an integer not less than 1;
obtains second feature data outputted through M convolutional layers of the convolutional neural network and different in size from the first feature data, M being an integer not less than 1 and not equal to N; and
performs anomaly detection on the image by using features indicated by the first feature data and the second feature data that are different in size, wherein
at least part of the M convolutional layers is shared with at least part of the N convolutional layers.