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US 2022/0044066 A1Published application
Systems and methods for a tailored neural network detector
AssigneeVintra, Inc.
InventorsAriel Amato, Angel Domingo Sappa, Carlo Gatta, Brent Boekestein
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Abstract
Various embodiments described herein provide for a neural network tailored, based on user-provided input data, to detect user-specified objects in image data. An architecture of an embodiment may use unlabeled data from the user, such as a set of images from a video camera stream, while parameters of a tailored neural network (CNN) are trained or adapted.
Claims21
(canceled)
A method comprising:
- processing, by one or more hardware processors, raw image data using a first trained neural network to produce a first initial set of region of interest (ROI) pairs, each ROI pair comprising a detected ROI for the raw image data and a detected region label classifying the detected ROI;
- processing, by the one or more hardware processors, the raw image data using a second trained neural network to produce a second initial set of ROI pairs;
- generating, by the one or more hardware processors, a first intermediate set of ROI pairs by combining the first initial set of ROI pairs and the second initial set of ROI pairs;
- evaluating, by the one or more hardware processors, the first intermediate set of ROI pairs using a set of expert classifiers to produce a set of confidence levels for the first intermediate set of ROI pairs;
- identifying, by the one or more hardware processors, first and second subsets of ROI pairs, in the first intermediate set of ROI pairs, based on the set of confidence levels, each ROI pair in the first subset of ROI pairs having a confidence level that does not satisfy a first reference confidence level criterion, and each ROI pair in the second subset of ROI pairs having a confidence level that satisfies the first reference confidence level criterion;
- sending, by the one or more hardware processors, the first subset of ROI pairs to a labeling system that uses a human individual to confirm or modify a particular detected region label of a particular ROI pair in the first subset of ROI pairs;
- receiving, by the one or more hardware processors, a set of human-confirmed ROI pairs received from the labeling system;
- and
- generating, by the one or more hardware processors, a second intermediate set of ROI pairs based on the set of human-confirmed ROI pairs.
6 more elements
A system comprising:
- a memory storing instructions;
- and
- one or more hardware processors communicatively coupled to the memory and configured by the instructions to perform operations comprising:
- processing raw image data using a first trained neural network to produce a first initial set of region of interest (ROI) pairs, each ROI pair comprising a detected ROI for the raw image data and a detected region label classifying the detected ROI;
- processing the raw image data using a second trained neural network to produce a second initial set of ROI pairs;
- generating a first intermediate set of ROI pairs by combining the first initial set of ROI pairs and the second initial set of ROI pairs;
- evaluating the first intermediate set of ROI pairs using a set of expert classifiers to produce a set of confidence levels for the first intermediate set of ROI pairs;
- identifying first and second subsets of ROI pairs, in the first intermediate set of ROI pairs, based on the set of confidence levels, each ROI pair in the first subset of ROI pairs having a confidence level that does not satisfy a first reference confidence level criterion, and each ROI pair in the second subset of ROI pairs having a confidence level that satisfies the first reference confidence level criterion;
- sending the first subset of ROI pairs to a labeling system that uses a human individual to confirm or modify a particular detected region label of a particular ROI pair in the first subset of ROI pairs;
- receiving a set of human-confirmed ROI pairs received from the labeling system;
- and
- generating a second intermediate set of ROI pairs based on the set of human-confirmed ROI pairs.
9 more elements
A non-transitory computer storage medium comprising instructions that, when executed by a hardware processor of a device, cause the device to perform operations comprising:
- processing raw image data using a first trained neural network to produce a first initial set of region of interest (ROI) pairs, each ROI pair comprising a detected ROI for the raw image data and a detected region label classifying the detected ROI;
- processing the raw image data using a second trained neural network to produce a second initial set of ROI pairs;
- generating a first intermediate set of ROI pairs by combining the first initial set of ROI pairs and the second initial set of ROI pairs;
- evaluating the first intermediate set of ROI pairs using a set of expert classifiers to produce a set of confidence levels for the first intermediate set of ROI pairs;
- identifying first and second subsets of ROI pairs, in the first intermediate set of ROI pairs, based on the set of confidence levels, each ROI pair in the first subset of ROI pairs having a confidence level that does not satisfy a first reference confidence level criterion, and each ROI pair in the second subset of ROI pairs having a confidence level that satisfies the first reference confidence level criterion;
- sending the first subset of ROI pairs to a labeling system that uses a human individual to confirm or modify a particular detected region label of a particular ROI pair in the first subset of ROI pairs
- receiving a set of human-confirmed ROI pairs received from the labeling system;
- and
- generating a second intermediate set of ROI pairs based on the set of human-confirmed ROI pairs.
6 more elements
Dependencies are inferred from the claim text.