Method and device of neural network operations using a grid generator for converting modes according to classes of areas to satisfy level 4 of autonomous vehicles
First Claim
1. A method for neural network operations by using a grid generator, comprising steps of:
- (a) a computing device, if a test image is acquired, instructing a non-object detector to acquire non-object location information for testing, including information on where non objects for testing are located on the test image, and class information of the non-objects for testing, including information on classes of the non-objects for testing, by detecting the non-objects for testing on the test image;
(b) the computing device instructing the grid generator to generate section information, which includes information on a plurality of subsections in the test image, by referring to the non-object location information for testing;
(c) the computing device instructing a neural network to determine parameters for testing, to be used for applying the neural network operations to either (i) at least part of the subsections including each of the objects for testing and each of non-objects for testing corresponding to said each of the objects for testing, or (ii) each of sub-regions, in each of said at least part of the subsections, where said each of the non-objects for testing is located, by referring to parameters for training which have been learned by using information on non-objects for training whose corresponding class information is same as or similar to that of the non-objects for testing; and
(d) the computing device instructing the neural network to apply the neural network operations to the test image by using each of the parameters for testing, corresponding to each of said at least part of the subsections, to thereby generate one or more neural network outputs.
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Abstract
A method of neural network operations by using a grid generator is provided for converting modes according to classes of areas to satisfy level 4 of autonomous vehicles. The method includes steps of: (a) a computing device, if a test image is acquired, instructing a non-object detector to acquire non-object location information for testing and class information of the non-objects for testing by detecting the non-objects for testing on the test image; (b) the computing device instructing the grid generator to generate section information by referring to the non-object location information for testing; (c) the computing device instructing a neural network to determine parameters for testing; (d) the computing device instructing the neural network to apply the neural network operations to the test image by using each of the parameters for testing, to thereby generate one or more neural network outputs.
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Citations
20 Claims
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1. A method for neural network operations by using a grid generator, comprising steps of:
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(a) a computing device, if a test image is acquired, instructing a non-object detector to acquire non-object location information for testing, including information on where non objects for testing are located on the test image, and class information of the non-objects for testing, including information on classes of the non-objects for testing, by detecting the non-objects for testing on the test image; (b) the computing device instructing the grid generator to generate section information, which includes information on a plurality of subsections in the test image, by referring to the non-object location information for testing; (c) the computing device instructing a neural network to determine parameters for testing, to be used for applying the neural network operations to either (i) at least part of the subsections including each of the objects for testing and each of non-objects for testing corresponding to said each of the objects for testing, or (ii) each of sub-regions, in each of said at least part of the subsections, where said each of the non-objects for testing is located, by referring to parameters for training which have been learned by using information on non-objects for training whose corresponding class information is same as or similar to that of the non-objects for testing; and (d) the computing device instructing the neural network to apply the neural network operations to the test image by using each of the parameters for testing, corresponding to each of said at least part of the subsections, to thereby generate one or more neural network outputs. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computing device for neural network operations by using a grid generator, comprising:
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at least one memory that stores instructions; and at least one processor configured to execute the instructions to;
perform processes of (I) instructing a non-object detector to acquire non-object location information for testing, including information on where non-objects for testing are located on a test image, and class information of the non-objects for testing, including information on classes of the non-objects for testing, by detecting the non-objects for testing on the test image, (II) instructing the grid generator to generate section information, which includes information on a plurality of subsections in the test image, by referring to the non-object location information for testing, (III) instructing a neural network to determine parameters for testing, to be used for applying the neural network operations to either (i) at least part of the subsections including each of the objects for testing and each of non-objects for testing corresponding to said each of the objects for testing, or (ii) each of sub-regions, in each of said at least part of the subsections where said each of the non-objects for testing is located, by referring to parameters for training which have been learned by using information on non-objects for training whose corresponding class information is same as or similar to that of the non-objects for testing, and (IV) instructing the neural network to apply the neural network operations to the test image by using each of the parameters for testing, corresponding to each of said at least part of the subsections, to thereby generate one or more neural network outputs. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification