Method and device for generating image data set and learning method and learning device using the same

Abstract

The invention relates to a method and computing device for generating an image data set for learning to be used for detecting obstruction in autonomous driving circumstances, and a learning method andlearning device using the same. The method includes steps of: a computing device (a) obtaining a first original image indicating a driving situation, and a first segmentation ground truth (GT) imagecorresponding to the first original image; (b) obtaining a second original image including a specific object, and a second segmentation GT image which includes segmentation information for the specific object and corresponds to the second original image; (c) obtaining a third original image by cutting a portion corresponding to the specific object, and a third segmentation GT image by cutting pixels corresponding to an area where the specific object is located; and (d) creating the data set for learning which includes a fourth original image and a fourth segmentation GT image corresponding tothe fourth original image.

Description

technical field [0001] The present invention relates to a method and a computing device for generating the above-mentioned learning data set for detecting the above-mentioned obstacle in an automatic driving situation, and a learning method and a learning device using the same. Background technique [0002] Deep Convolutional Neural Networks (Deep CNNs) are at the heart of amazing developments in the field of deep learning. CNNs have been used to solve text recognition problems in the 90s, but their widespread use today is due to recent research results. The above-mentioned deep CNN won the championship in the ImageNet image classification competition in 2012. Convolutional neural networks have since become very useful tools in the field of Machine Learning. [0003] In addition, image segmentation (Image segmentation) is a method of receiving an image (training image or test image) as input and creating a label (label) as output. Recently, with the attention of deep lear...

AI Technical Summary

Problems solved by technology

That is, it is difficult to obtain learning images for special objects that do not often appear on the road from general driving image data

For example, it is easier to obtain image data for people, bicycles, and vehicles from general driving image data, so in order to improve detection performance, learning can be performed on images containing these objects, but it is difficult to obtain images for tigers or crocodiles from general driving image data data, so there is a problem that it is not easy to learn to improve the detection performance of special objects

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