Anomaly detection using a convolutional neural network and feature based memories

A system (100) for anomaly detection comprising:

• a memory (144);
• and
• at least one processor (142) communicatively coupled to the memory (144), wherein the at least one processor (142) is configured to execute instructions stored in the memory to:
• receive an image of an object (310);
• input the image to a pre-trained convolutional neural network including a plurality of intermediate layers (320);
• extract feature maps output at the plurality of intermediate layers (330);
• input 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);
• create 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);
• resize the two-dimensional heatmaps to the dimensions of the image (360);
• obtain an anomaly heat map by performing a weighted average of the resized two-dimensional heatmaps (370);
• and
• identify an anomaly in the object by discarding heatmap intensities below a threshold in the anomaly heatmap (380).