A learning method of a CNN capable of detecting one or more lanes, comprising steps of: (a) a learning device, if at least one training image is acquired, applying one or more convolution operations to the training image, to thereby generate at least one feature map, and generating lane candidate information which represents information on lane candidates estimated by referring to segmentation scores generated by using the feature map; (b) the learning device generating a first pixel data map including information on each of pixels in the training image and on its corresponding piece of first data, wherein one or more main subsets selected from the first data include each value generated from calculating each distance from each position of each of the pixels to its corresponding nearest first lane candidate along a first direction on a left side which is an horizontal direction parallel to a direction of rows of the training image by using a direct regression, wherein said main subsets selected from the first data include pixels that are within a certain threshold away from the pixels of the nearest first lane candidate along the first direction, and generating a second pixel data map including information on each of the pixels and on its corresponding piece of second data, wherein one or more main subsets selected from the second data include each value generated from calculating each distance from said each position of each of the pixels to its corresponding nearest second lane candidate along a second direction on a right side which is an horizontal direction parallel to the direction of the rows of the training image by using the direct regression, wherein said main subsets selected from the second data include pixels that are within the certain threshold away from the pixels of the nearest second lane candidate along the second direction; and (c) the learning device detecting the lanes based on a first part of the pixels having its value less than the certain threshold in the first pixel data map and the second pixel data map. (a) a learning device, if at least one training image is acquired, applying one or more convolution operations to the training image, to thereby generate at least one feature map, and generating lane candidate information which represents information on lane candidates estimated by referring to segmentation scores generated by using the feature map; (b) the learning device generating a first pixel data map including information on each of pixels in the training image and on its corresponding piece of first data, wherein one or more main subsets selected from the first data include each value generated from calculating each distance from each position of each of the pixels to its corresponding nearest first lane candidate along a first direction on a left side which is an horizontal direction parallel to a direction of rows of the training image by using a direct regression, wherein said main subsets selected from the first data include pixels that are within a certain threshold away from the pixels of the nearest first lane candidate along the first direction, and generating a second pixel data map including information on each of the pixels and on its corresponding piece of second data, wherein one or more main subsets selected from the second data include each value generated from calculating each distance from said each position of each of the pixels to its corresponding nearest second lane candidate along a second direction on a right side which is an horizontal direction parallel to the direction of the rows of the training image by using the direct regression, wherein said main subsets selected from the second data include pixels that are within the certain threshold away from the pixels of the nearest second lane candidate along the second direction; and (c) the learning device detecting the lanes based on a first part of the pixels having its value less than the certain threshold in the first pixel data map and the second pixel data map.