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US 12,608,975 B2Granted
Multi-scale model ensemble for classification of objects
AssigneeGoogle Inc.
InventorsYaojie Liu, Wen-Sheng Chu
Life of this patent
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Abstract
Provided is a multi-scale model ensemble for detection of objects in images. The model ensemble can be applied, for example, in the context of performing object identification activities, such as positively identifying desired objects in image data or video data using a variety of different crop levels.
Claims20
A method for detecting an object in an image, the method comprising:
- receiving, by a computing system comprising at least one electronic processor, an input image;
- generating, by the computing system, an image set from the input image, wherein the image set comprises a plurality of images that depict different portions of the input image, at least one of the plurality of images being a cropped image generated based on cropping the input image to depict a cropped portion of the input image;
- respectively processing, by the computing system, the plurality of images with a plurality of machine-learned models to generate a plurality of outputs respectively from the plurality of machine-learned models, the cropped image being processed by a first machine-learned model of the plurality of machine-learned models and at least one other image of the plurality of images being processed by a second machine-learned model of the plurality of machine-learned models;
- determining, by the computing system, an object detection prediction for the input image based at least in part on the plurality of outputs respectively from the plurality of machine-learned models;
- and
- performing, by the computing system, at least one action based at least in part on the object detection prediction for the input image.
3 more elements
A computer system for detecting an object, the computer system comprising:
- at least one electronic processor;
- one or more non-transitory computer-readable media that collectively store:
- a multi-scale model ensemble comprising a plurality of machine-learned models;
- and
- instructions that, when executed by the computing system, cause the computer system to perform operations, the operations comprising:
- receiving an input image;
- generating an image set from the input image, wherein the image set comprises a plurality of images that depict different portions of the input image, at least one of the plurality of images being a cropped image generated based on cropping the input image to depict a cropped portion of the input image;
- respectively processing the plurality of images with the plurality of machine-learned models to generate a plurality of outputs respectively from the plurality of machine-learned models, the cropped image being processed by a first machine-learned model of the plurality of machine-learned models and at least one other image of the plurality of images being processed by a second machine-learned model of the plurality of machine-learned models;
- determining an object detection prediction for the input image based at least in part on the plurality of outputs respectively from the plurality of machine-learned models;
- and
- performing at least one action based at least in part on the object detection prediction for the input image.
8 more elements
A user device for detecting an object, the user device comprising:
- at least one electronic processor;
- one or more non-transitory computer-readable media that collectively store:
- a multi-scale model ensemble comprising a plurality of machine-learned models;
- and
- instructions that, when executed by the computing system cause the computer system to perform operations, the operations comprising:
- receiving an input image;
- generating an image set from the input image, wherein the image set comprises a plurality of images that depict different portions of the input image, at least one of the plurality of images being a cropped image generated based on cropping the input image to depict a cropped portion of the input image;
- and
- respectively processing the plurality of images with the plurality of machine-learned models to generate a plurality of outputs respectively from the plurality of machine-learned models, the cropped image being processed by a first machine-learned model of the plurality of machine-learned models and at least one other image of the plurality of images being processed by a second machine-learned model of the plurality of machine-learned models;
- determining an object detection prediction for the input image based at least in part on the plurality of outputs respectively from the plurality of machine-learned models;
- and
- performing at least one action based at least in part on the object detection prediction for the input image.
9 more elements
Dependencies are inferred from the claim text.