首页> 外国专利> 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

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

机译:用于将RGB训练图像集策略性转换为非RGB训练图像集的学习方法和学习装置,用于通过使用循环gan,结果输入和结果求和来对非RGB格式的图像对象进行对象检测。数据

摘要

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.
机译:提供了一种方法,该方法通过使用循环GAN在目标颜色空间中学习将带注释的RGB图像转换为带注释的非RGB图像,以及用于域自适应的方法,该方法能够降低注释成本并优化客户需求。该方法包括以下步骤:学习设备将RGB格式的第一图像转换为非RGB格式的第二图像,确定第二图像具有主要还是次要非RGB格式,并将第二图像转换为RGB格式的第三张图像;将非RGB格式的第四图像转换为RGB格式的第五图像,确定第五图像具有主RGB格式还是辅助RGB格式,并将第五图像转换为非RGB格式的第六图像。此外,通过该方法,即使在虚拟驾驶环境下也可以生成训练数据。

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