Anomaly detection using a convolutional neural network and feature based memories
Embodiments of the present disclosure discuss a system and a method for anomaly detection in an object. Conventional systems of defect detection require labeled training data. Further, current anomaly detection systems cannot be easily incorporated into factory production systems. Embodiments of the present disclosure address this problem by using a convolutional neural network (CNN) and feature based memories such as neural network based memories for anomaly detection. The method described in the embodiments uses neural network based memories to generate memory feature maps from feature maps extracted at intermediate layers of the CNN. Subsequently, heatmaps are created by calculating feature differences between the feature maps and the memory feature maps. Weighted averaging and post processing is performed on the heatmaps to identify the anomaly in the object.
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 ).
2. The method as claimed in claim 1 , wherein the image is received from an image acquisition unit ( 120 ).
3. The method as claimed in claim 2 , wherein the image acquisition unit ( 120 ) comprises a plurality of cameras.
4. The method as claimed in claim 1 , wherein the pretrained neural network based memories include an auto-encoder.
5. The method as claimed in claim 1 , wherein the pretrained neural network based memories includes a generative adversarial network.
6. The method as claimed in claim 1 , wherein the threshold is calculated using a business logic.
7. The method as claimed in claim 1 , wherein the pretrained neural network based memory corresponding to each intermediate layer is trained using feature maps extracted at the layer when images of a training set are input to the pretrained convolutional neural network.
8. The method as claimed in claim 1 , comprising classifying the image based on the identified anomaly.
9. The method as claimed in claim 1 , wherein the object is classified as anomalous based on the identified anomaly.
10. 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 ).
11. The system as claimed in claim 10 , wherein the image is received from an image acquisition unit ( 120 ).
12. The system as claimed in claim 11 , wherein the image acquisition unit ( 120 ) comprises a plurality of cameras.
13. The system as claimed in claim 10 , wherein the pretrained neural network based memories include an auto-encoder.
14. The system as claimed in claim 10 , wherein the pretrained neural network based memories include a generative adversarial network.
15. The system as claimed in claim 10 , wherein the threshold is calculated using a business logic.
16. The system as claimed in claim 10 , wherein the pretrained neural network based memory corresponding to each intermediate layer is trained using feature maps extracted at the layer when images of a training set are input to the pretrained convolutional neural network.
17. The system as claimed in claim 10 , comprising classifying the image based on the identified anomaly.
18. The system as claimed in claim 10 , wherein the object is classified as anomalous based on the identified anomaly.