US 12,437,523 B2
Anomaly detection using a convolutional neural network and feature based memories
Sekar Udayamurthy, Chennai (IN); Mohan Nikam, Chennai (IN); Vinodh Venkatesh, Chennai (IN); and Krishna Iyengar, Chennai (IN)
Assigned to JIDOKA TECHNOLOGIES PRIVATE LIMITED, Chennai (IN)
Appl. No. 18/035,942
Filed by JIDOKA TECHNOLOGIES PRIVATE LIMITED, Chennai (IN)
PCT Filed Apr. 25, 2022, PCT No. PCT/IN2022/050385
§ 371(c)(1), (2) Date May 9, 2023,
PCT Pub. No. WO2022/229976, PCT Pub. Date Nov. 3, 2022.
Claims priority of application No. 202141019200 (IN), filed on Apr. 26, 2021.
Prior Publication US 2023/0410484 A1, Dec. 21, 2023
Int. Cl. G06V 10/776 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/776 (2022.01) [G06V 10/7715 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01)] 18 Claims
OG exemplary drawing
 
1. A method for anomaly detection comprising:
receiving, by a processor, an image of an object (310);
inputting, by the processor, the image to a pre-trained convolutional neural network including a plurality of intermediate layers (320);
extracting, by the processor, feature maps output at the plurality of intermediate layers (330);
inputting, by the processor, the feature maps extracted at the plurality of intermediate layers to pretrained neural network based memories corresponding to the plurality of intermediate layers to generate memory feature maps (340);
creating, by the processor, a two-dimensional heatmap corresponding to each intermediate layer of the plurality of intermediate layers by calculating a feature difference between the feature map extracted at the intermediate layer and the memory feature map generated by the pretrained neural network based memory corresponding to the intermediate layer (350);
resizing, by the processor, the two-dimensional heatmaps to the dimensions of the image (360);
obtaining, by the processor, an anomaly heat map by performing a weighted average of the resized two-dimensional heatmaps (370); and
identifying, by the processor, an anomaly in the object by discarding heatmap intensities below a threshold in the anomaly heatmap (380).