Abstract: 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.

Abstract: Embodiments of the present disclosure discuss a system [100] and a method [200] for refining training images for object detection. Conventional systems provide inaccurate predictions of defects because of erroneous labeling of defects in training images. Further, existing systems focus on improving the algorithms used for object detection. Embodiments of the present disclosure address these problems by using supervised learning techniques and refining the training data provided to train the algorithms. The system [100] refines the training images using predictions of a first model and providing the predictions for human review, and subsequently uses the refined training images to create a second model that can be used in a production environment of an enterprise for object detection.

Abstract: 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.