|Keywords||Anomaly detection, Nearest-neighbors, Feature pyramid|
|Development stage||Looking for industrial POC|
A key human talent is the ability to detect the novel images that deviate from previous patterns triggering particular vigilance on the part of the human agent. Due to the importance of this task, allowing computers to detect anomalies is a key task for artificial intelligence. An example of this as a real world application is fault detection at assembly lines in which the challenge would be to replace a human operator with computer vision solutions.
The algorithms for anomaly detection will be trained to deal with unexpected images that were unavailable during the training set. In addition, and in particular, for the visual anomalies detection – the localization of the part of the anomalous image segment is crucial for building trust between operators and novel AI systems.
Our researcher has developed a new method for solving the task of sub-image anomaly detection and segmentation. Our method does not require an extended training stage; it is fast robust and achieves state-of-the-art performance. The method has been extensively evaluated on an industrial product dataset as well as a surveillance dataset in a campus setting. Our method achieves state-of-the-art performance both on the image-level and pixel-level anomaly detection.
In the experiments conducted, the researcher compared the SPADE method against several methods that were recently introduced, as well as longer standing baselines such as OCSVM. For each setting, we compared against the methods that reported the suitable metric.
Anomaly detection is can be useful across an array of industries and for a variety of purposes, including IT and DevOps, manufacturing, healthcare, banking and finance, and in the public sector.