Synthetic traffic object generator
First Claim
1. A method comprising:
- obtaining, using a deep learning module that is implemented by a processor configured to execute instructions stored in a non-transitory memory, image data from an image;
assigning, using the deep learning module, a set of default parameters to the image data, wherein the set of default parameters include values associated with at least one of a weather condition of the image and a defect condition of an object of the image;
generating, using the deep learning module, a set of predicted parameters based on the image data;
determining, using the deep learning module, an error for each parameter of the set of predicted parameters, wherein the error is based on a value of the parameter of the set of predicted parameters and a value of a corresponding default parameter of the set of default parameters; and
adjusting, using the deep learning module and in response to the error for one parameter of the set of predicted parameters being greater than an error threshold, a weight of a corresponding connection of the deep learning module.
1 Assignment
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Accused Products
Abstract
A method for generating images of traffic objects using deep learning is disclosed. The method includes obtaining image data from an image. The method includes assigning a set of default parameters to the image data, and the set of default parameters include values associated with at least one of a weather condition of the image and a defect condition of an object of the image. The method includes generating a set of predicted parameters based on the image data. The method includes determining an error for each parameter of the set of predicted parameters, and the error is based on a value of the parameter of the set of predicted parameters and a value of a corresponding default parameter of the set of default parameters. The method includes adjusting a weight of a corresponding connection of the deep learning module.
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Citations
16 Claims
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1. A method comprising:
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obtaining, using a deep learning module that is implemented by a processor configured to execute instructions stored in a non-transitory memory, image data from an image; assigning, using the deep learning module, a set of default parameters to the image data, wherein the set of default parameters include values associated with at least one of a weather condition of the image and a defect condition of an object of the image; generating, using the deep learning module, a set of predicted parameters based on the image data; determining, using the deep learning module, an error for each parameter of the set of predicted parameters, wherein the error is based on a value of the parameter of the set of predicted parameters and a value of a corresponding default parameter of the set of default parameters; and adjusting, using the deep learning module and in response to the error for one parameter of the set of predicted parameters being greater than an error threshold, a weight of a corresponding connection of the deep learning module. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising:
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a deep learning module that is implemented by a processor configured to execute instructions stored in a non-transitory memory, wherein the instructions include; obtaining, using the deep learning module, image data from an image; assigning, using the deep learning module, a set of default parameters to the image data, wherein the set of default parameters include values associated with at least one of a weather condition of the image and a defect condition of an object of the image; generating, using the deep learning module, a set of predicted parameters based on the image data; determining, using the deep learning module, an error for each parameter of the set of predicted parameters, wherein the error is based on a value of the parameter of the set of predicted parameters and a value of a corresponding default parameter of the set of default parameters; and adjusting, using the deep learning module and in response to the error for one parameter of the set of predicted parameters being greater than an error threshold, a weight of a corresponding connection of the deep learning module. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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Specification