A method for learning transformation of an annotated RGB image into an annotated Non-RGB image, in target color space, by using a cycle GAN and for domain adaptation capable of reducing annotation cost and optimizing customer requirements is provided. The method includes steps of: a learning device transforming a first image in an RGB format to a second image in a non-RGB format, determining whether the second image has a primary or a secondary non-RGB format, and transforming the second image to a third image in the RGB format; transforming a fourth image in the non-RGB format to a fifth image in the RGB format, determining whether the fifth image has a primary RGB format or a secondary RGB format, and transforming the fifth image to a sixth image in the non-RGB format. Further, by the method, training data can be generated even with virtual driving environments.
LEARNING METHOD AND LEARNING DEVICE FOR STRATEGIC TRANSFORMING RGB TRAINING IMAGE SETS INTO NON-RGB TRAINING IMAGE SETS, TO BE USED FOR LEARNING OBJECT DETECTION ON OBJECTS OF IMAGES IN NON-RGB FORMAT, BY USING CYCLE GAN, RESULTING IN SIGNIFICANTLY REDUCING COMPUTATIONAL LOAD AND REUSING DATA