| CPC G06T 7/10 (2017.01) [G06T 2207/10088 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30096 (2013.01)] | 14 Claims |
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8. A method for image segmentation performed by a computing device that includes one or more processors and a memory for storing one or more programs executed by the one or more processors, the method comprising:
acquiring one or more images in which an object is photographed; and
performing segmentation on the one or more images using a segmentation model which is deep learned through a plurality of images,
wherein the segmentation model is a U-Net-based model including a first type module based on depth-wise separable convolution (DSC) and a second type module based on global context network (GCNet),
wherein the first type module is configured to include a plurality of depth-wise convolution layer blocks for extracting feature information of a feature map and a plurality of point-wise convolution layer blocks including a first point-wise convolution layer block and a second point-wise convolution layer block for controlling the number of channels of the feature map,
wherein the first type module is configured to:
calculate a map for extracting feature information by repeatedly applying the depth-wise convolution layer block and the point-wise convolution layer block to an input feature map, and, in calculating the map for extracting feature information, increase a number of output channels using a first point-wise convolution layer block of the plurality of point-wise convolution layer blocks and then adjust the increased number of the output channels to the number before increasing by using a second point-wise convolution layer block;
calculate a map for controlling the number of channels by applying only the point-wise convolution layer block to the input feature map; and
sum the map for extracting the feature information and the map for adjusting the number of channels and output a result of the summation.
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