US 2026/0073525 A1 · ANOMALY DETECTION METHOD BASED ON OUT-OF-DISTRIBUTION AND NON-TRANSITORY COMPUTER-READABLE MEDIUM

Abstract

This anomaly detection method, based on out-of-distribution techniques, is executed by a computing device. It starts by obtaining a training dataset containing various images, including a first image and multiple second images. The method segments objects and contexts in each image, calculating the similarity between the object in the first image and those in the second images. A candidate image is selected if its similarity exceeds a predefined threshold. The object from the first image is blended with the context of the candidate image to produce a blended image. A detection model is then trained using this dataset. Subsequently, in-distribution embeddings are generated, and a test embedding is created. The test sample is classified as an anomaly when the minimum distance between the in-distribution embeddings and the test embedding exceeds a default value.

Claims10

Dependent claims

An anomaly detection method based on out-of-distribution performed by a computing device and comprising:

obtaining a training dataset comprising a plurality of images, wherein the plurality of images comprises a first image and a plurality of second images;
segmenting an object and a context in each of the plurality of images;
calculating a similarity between the object in the first image and the object in each of the plurality of second images;
selecting a candidate image from the plurality of second images whose similarity exceeds a threshold;
blending the object in the first image with the context in the candidate image to generate a blended image;
training a detection model according to the training dataset and the blended image;
executing the detection model to generate a plurality of in-distribution embeddings according to the plurality of images and the blended image, and generate a test embedding according to a test sample;
calculating a plurality of distances between the in-distribution embeddings and the test embedding;
and
classifying the test sample as an anomaly when a minimum of the plurality of distances exceeds a default value.

7 more elements

A non-transitory computer-readable medium configured to store a plurality of instructions, wherein the plurality of instruction is performed by a computing device to cause a plurality of operations, comprising:

obtaining a training dataset comprising a plurality of images, wherein the plurality of images comprises a first image and a plurality of second images;
segmenting an object and a context in each of the plurality of images;
calculating a similarity between the object in the first image and the object in each of the plurality of second images;
selecting a candidate image from the plurality of second images whose similarity exceeds a threshold;
blending the object in the first image with the context in the candidate image to generate a blended image;
training a detection model according to the training dataset and the blended image;
executing the detection model to generate a plurality of in-distribution embeddings according to the plurality of images and the blended image, and generate a test embedding according to a test sample;
calculating a plurality of distances between the in-distribution embeddings and the test embedding;
and
classifying the test sample as an anomaly when a minimum of the plurality of distances exceeds a default value.

7 more elements

Dependencies are inferred from the claim text.

Anomaly detection method based on out-of-distribution and non-transitory computer-readable medium

Life of this patent

Anomaly detection method based on out-of-distribution and non-transitory computer-readable medium

Life of this patent

Abstract

Claims10