A method of attention-based lane detection without post-processing by using a lane mask is provided. The method includes steps of: a learning device instructing a CNN to acquire a final feature map which has been generated by applying convolution operations to an image, a segmentation score map, and an embedded feature map which have been generated by using the final feature map; instructing a lane masking layer to recognize lane candidates, generate the lane mask, and generate a masked feature map; instructing a convolutional layer to generate a lane feature map; instructing a first FC layer to generate a softmax score map and a second FC layer to generate lane parameters; and backpropagating loss values outputted from a multinomial logistic loss layer and a line fitting loss layer, to thereby learn parameters of the FC layers, and the convolutional layer. Thus, lanes at distance can be detected more accurately.