Abstract: Systems and methods for anomaly localization within content captured by a machine vision camera are disclosed herein. An example method includes receiving, at an application executing on a user computing device communicatively coupled to a machine vision camera, an image captured by the machine vision camera, the image including a target object with one or more localized anomalies. The example method further includes generating, by applying an unsupervised anomaly detection (UAD) module to the image, an anomaly heatmap corresponding to the image; and concatenating, by a concatenation module, the image and the anomaly heatmap into a multi-channel image. The example method also includes generating, by applying a supervised segmentation (SS) module to the multi-channel image, a hybrid output mask identifying the one or more localized anomalies within the image.

Abstract: An object analysis system is disclosed herein. The object analysis system may receive an input image that depicts an object. The object analysis system may determine, using a feature extraction model and from the input image, a first feature output that is associated with one or more features of the object. The feature extraction model may be trained based on reference images that depict reference objects that are a type of the object. The object analysis system may determine, using a classification model, that an anomaly status of the object is indicative of the object including an anomaly. The classification model may be trained based on the reference images. The object analysis system may determine, using an anomaly localization model, a location of the anomaly in the input image based on a second feature output of the convolutional neural network encoder. The anomaly localization model may be trained based on the reference images.