A method for learning an automatic parking device of a vehicle to be used for detecting an available parking area in a parking lot, comprising steps of: (a) if at least one parking lot image is acquired which is created by taking a shot of a nearby area of the vehicle from an upper part of the parking lot toward a ground of the parking lot, a learning device performing (i) a process of inputting the parking lot image into a segmentation network, to thereby allow the segmentation network to (i-1) output at least one convolution feature map by applying at least one convolution operation to the parking lot image via an encoder, (i-2) output at least one deconvolution feature map by applying at least one deconvolution operation to the convolution feature map via a decoder, and (i-3) output segmentation information, representing segmentation of one or more unoccupied parking regions and one or more occupied parking regions on the parking lot image, by applying at least one masking operation to the deconvolution feature map via a masking layer; (b) the learning device performing (i) a process of inputting the deconvolution feature map, corresponding to a specific ROI, into a regressor to thereby allow the regressor (i-1) to generate relative coordinates of one or more vertices of the specific ROI from a center of the specific ROI, and thus (i-2) to generate regression location information on at least one specific available parking region by applying at least one regression operation to the relative coordinates, wherein the specific available parking region among the unoccupied parking regions is determined as the specific ROI; and (c) the learning device performing a process of instructing a 1-st loss layer to calculate one or more 1-st losses by referring to the regression location information and its corresponding at least one ROI GT, and a process of learning the regressor via backpropagation using the 1-st losses such that the 1-st losses are minimized. (a) if at least one parking lot image is acquired which is created by taking a shot of a nearby area of the vehicle from an upper part of the parking lot toward a ground of the parking lot, a learning device performing (i) a process of inputting the parking lot image into a segmentation network, to thereby allow the segmentation network to (i-1) output at least one convolution feature map by applying at least one convolution operation to the parking lot image via an encoder, (i-2) output at least one deconvolution feature map by applying at least one deconvolution operation to the convolution feature map via a decoder, and (i-3) output segmentation information, representing segmentation of one or more unoccupied parking regions and one or more occupied parking regions on the parking lot image, by applying at least one masking operation to the deconvolution feature map via a masking layer; (b) the learning device performing (i) a process of inputting the deconvolution feature map, corresponding to a specific ROI, into a regressor to thereby allow the regressor (i-1) to generate relative coordinates of one or more vertices of the specific ROI from a center of the specific ROI, and thus (i-2) to generate regression location information on at least one specific available parking region by applying at least one regression operation to the relative coordinates, wherein the specific available parking region among the unoccupied parking regions is determined as the specific ROI; and (c) the learning device performing a process of instructing a 1-st loss layer to calculate one or more 1-st losses by referring to the regression location information and its corresponding at least one ROI GT, and a process of learning the regressor via backpropagation using the 1-st losses such that the 1-st losses are minimized.