US 2025/0166165 A1 · TRAINING SYSTEMS FOR SURFACE ANOMALY DETECTION

Abstract

It is recognized herein that deep-learning approaches to anomaly detection can require a large amount of training data to properly learn the task. It is further recognized herein that capturing images of anomalies can be particularly costly or impractical or, in some cases, impossible. For example, by definition, anomalies can be rare and, therefore, gathering enough samples to train a convolutional neural network can be tedious. Annotating anomalies that are depicted can also be an expensive and time-consuming task. In various examples, realistic synthetic images are generated that include plausible and annotated surface defects (anomalies). Such synthetic images are used to train an efficient anomaly segmentation network in a fully supervised manner.

Claims15

Dependent claims

A method comprising:

generating, by an anomaly texture generator, first color texture images that include respective surface anomalies;
obtaining 3D models of objects associated with the first color texture images;
based on the 3D models and the first color texture images associated with the 3D models, a rendering module generating first synthetic images of the respective objects, the first synthetic images defining the objects in a realistic scene, the objects of the first synthetic images each defining a surface and at least one anomaly on the surface of the respective object;
and
training an anomaly segmentation network, to detect anomalies, with the first synthetic images.

2 more elements

Dependencies are inferred from the claim text.

Training systems for surface anomaly detection

Life of this patent

Training systems for surface anomaly detection

Life of this patent

Abstract

Claims15