A computer implemented learning method for detecting at least one lane based on a convolutional neural network, comprising steps of: (a) a learning device, if an input image is obtained, comprising an encoding layer to apply one or more convolution operations to the input image to thereby obtain one or more encoded feature maps, and comprising a decoding layer to apply one or more deconvolution operations to a specific encoded feature map outputted from the encoding layer to thereby obtain a segmentation result including information on one or more lane candidate pixels in the input image; (b) the learning device comparing each of probability scores of each of the lane candidate pixels with a predetermined first threshold and comparing each of the probability scores with a predetermined second threshold smaller than the first threshold, then if one or more probability scores of one or more first parts of the lane candidate pixels are equal to or greater than the first threshold, classifying the first parts of the lane candidate pixels as pixels of one or more first strong lines, and if one or more probability scores of one or more second parts of the lane candidate pixels are less than the first threshold and are equal to or greater than the second threshold, classifying the second parts as pixels of one or more weak lines; and (c) the learning device, if one or more values of distance between (i) one or more pixels among the pixels of the weak lines and (ii) at least part of the pixels of the first strong lines are less than a predetermined first distance, classifying the pixels as pixels of one or more additional strong lines, and detecting the lane corresponding to the pixels of the first strong lines and the pixels of the additional strong lines by using Polynomial fitting algorithm or spline fitting algorithm, wherein the predetermined first distance is a distance between any two closest pixels or an n-pixel distance. (a) a learning device, if an input image is obtained, comprising an encoding layer to apply one or more convolution operations to the input image to thereby obtain one or more encoded feature maps, and comprising a decoding layer to apply one or more deconvolution operations to a specific encoded feature map outputted from the encoding layer to thereby obtain a segmentation result including information on one or more lane candidate pixels in the input image; (b) the learning device comparing each of probability scores of each of the lane candidate pixels with a predetermined first threshold and comparing each of the probability scores with a predetermined second threshold smaller than the first threshold, then if one or more probability scores of one or more first parts of the lane candidate pixels are equal to or greater than the first threshold, classifying the first parts of the lane candidate pixels as pixels of one or more first strong lines, and if one or more probability scores of one or more second parts of the lane candidate pixels are less than the first threshold and are equal to or greater than the second threshold, classifying the second parts as pixels of one or more weak lines; and (c) the learning device, if one or more values of distance between (i) one or more pixels among the pixels of the weak lines and (ii) at least part of the pixels of the first strong lines are less than a predetermined first distance, classifying the pixels as pixels of one or more additional strong lines, and detecting the lane corresponding to the pixels of the first strong lines and the pixels of the additional strong lines by using Polynomial fitting algorithm or spline fitting algorithm, wherein the predetermined first distance is a distance between any two closest pixels or an n-pixel distance.