Method and device for generating image data set to be used for learning CNN capable of detecting obstruction in autonomous driving circumstance, and testing method, and testing device using the same

A test method of generating at least one image data set to be used for detecting at least one obstruction in one or more autonomous driving circumstances, comprising:

• (a) a testing device, on condition that (I) a learning device has acquired (i) an original image for training representing a road driving circumstance and (ii) a synthesized label for training obtained by using an original label for training corresponding to the original image for training and an additional label for training corresponding to an arbitrary specific object for training, wherein the arbitrary specific object for training does not relate to the original image for training;
• (II) the learning device has supported a first CNN module to generate a synthesized image for training using the original image for training and the synthesized label for training, wherein the synthesized image for training is created by combining (i) an image of the arbitrary specific object for training corresponding to the additional label for training and (ii) the original image for training, (III) the learning device has supported, a second CNN module to acquire the synthesized image for training and to generate a first output value which represents a probability as to whether the synthesized image for training is fake or real, and further has supported the second CNN module to acquire an arbitrary real image which has not been synthesized, and to generate a second output value which represents a probability as to whether the arbitrary real image is fake or real;
• and (IV) the learning device has supported the second CNN module to generate a first loss value by comparing the first output value to a first GT value, and a second loss value by comparing the second output value to a second GT value, and has supported the first and the second CNN modules to be learned by backpropagating each of the first and the second loss values through the second CNN module and the first CNN module, acquiring (i) an original image for testing representing a road driving circumstance and (ii) a synthesized label for testing obtained by using an original label for testing corresponding to the original image for testing and an additional label for testing corresponding to an arbitrary specific object for testing, wherein the arbitrary specific object for testing does not relate to the original image for testing;
• and
• (b) the testing device supporting the first CNN module to generate a synthesized image for testing using the original image for testing and the synthesized label for testing,
• wherein the synthesized image for testing is created by combining (i) an image of the arbitrary specific object for testing corresponding to the additional label for testing and (ii) the original image for testing.

A device of generating at least one image data set to be used for learning CNN capable of detecting at least one obstruction in one or more autonomous driving circumstances, comprising:

• a communication part for acquiring (i) an original image representing a road driving circumstance and (ii) a synthesized label obtained by using an original label corresponding to the original image and an additional label corresponding to an arbitrary specific object, wherein the arbitrary specific object does not relate to the original image;
• a processor for performing a process of
• (I) supporting a first CNN module to generate a synthesized image using the original image and the synthesized label, wherein the synthesized image is created by combining (i) an image of the arbitrary specific object corresponding to the additional label and (ii) the original image;
• (II) supporting a second CNN module to acquire the synthesized image and to generate a first output value which represents a probability as to whether the synthesized image is fake or real, and supporting the second CNN module to acquire an arbitrary real image which has not been synthesized, and to generate a second output value which represents a probability as to whether the arbitrary real image is fake or real;
• and
• (III) supporting the second CNN module to generate a first loss value by comparing the first output value to a first GT value, and a second loss value by comparing the second output value to a second GT value, and supporting the first and the second CNN modules to be learned by backpropagating each of the first and the second loss values through the second CNN module and the first CNN module.

A test device of generating at least one image data set to be used for detecting at least one obstruction in one or more autonomous driving circumstances, comprising:

• a communication part, on condition that (I) a learning device has acquired (i) an original image for training representing a road driving circumstance and (ii) a synthesized label for training obtained by using an original label for training corresponding to the original image for training and an additional label for training corresponding to an arbitrary specific object for training, wherein the arbitrary specific object for training does not relate to the original image for training;
• (II) the learning device has supported a first CNN module to generate a synthesized image for training using the original image for training and the synthesized label for training, wherein the synthesized image for training is created by combining (i) an image of the arbitrary specific object for training corresponding to the additional label for training and (ii) the original image for training, (III) the learning device has supported, a second CNN module to acquire the synthesized image for training and to generate a first output value which represents a probability as to whether the synthesized image for training is fake or real, and further has supported the second CNN module to acquire an arbitrary real image which has not been synthesized, and to generate a second output value which represents a probability as to whether the arbitrary real image is fake or real;
• and (IV) the learning device has supported the second CNN module to generate a first loss value by comparing the first output value to a first GT value, and a second loss value by comparing the second output value to a second GT value, and has supported the first and the second CNN modules to be learned by backpropagating each of the first and the second loss values through the second CNN module and the first CNN module, for acquiring (i) an original image for testing representing a road driving circumstance and (ii) a synthesized label for testing obtained by using an original label for testing corresponding to the original image for testing and an additional label for testing corresponding to an arbitrary specific object for testing, wherein the arbitrary specific object for testing does not relate to the original image for testing;
• and
• a processor for performing a process of (I) supporting the first CNN module to generate a synthesized image for testing using the original image for testing and the synthesized label for testing,
• wherein the synthesized image for testing is created by combining (i) an image of the arbitrary specific object for testing corresponding to the additional label for testing and (ii) the original image for testing.