A learning method for a CNN (Convolutional Neural Network) capable of encoding at least one training image with one or more multiple feeding layers (410, 420), wherein the CNN includes a 1st to an n-th convolutional layers (310, 320, 330), which respectively generate a 1st to an n-th main feature maps (312, 322, 332) by applying one or more convolution operations to the training image, and a 1st to an h-th feeding layers (410, 420) respectively corresponding to h convolutional layers among the n convolutional layers (310, 320, 330), and wherein the h is an integer from 1 to (n-1), comprising steps of: (a) a learning device acquiring the training image; and (b) the learning device instructing the 1st convolutional layer to apply the convolution operations to the training image to thereby generate the 1st main feature map and instructing each of the 2nd to the n-th convolutional layers (320,330) to apply operations to the main feature map (312, 322) from its previous convolutional layer to thereby generate each of the 2nd to the n-th main feature maps (312, 322, 332), wherein the learning device instructs a k-th convolutional layer which is one of the h convolutional layers (320, 330) respectively corresponding to the 1st to the h-th feeding layers (410, 420), the h convolutional layers (320, 330) being selected from the 2nd to the n-th convolutional layers (310, 320, 330), to (i) acquire a (k-1)-th main feature map from a (k-1)-th convolutional layer and an m-th sub feature map, which is obtained by resizing the training image into a size of the (k-1)-th main feature map, from an m-th feeding layer corresponding to the k-th convolutional layer, (ii) generate a (k-1)-th integrated feature map by integrating the (k-1)-th main feature map and the m-th sub feature map, and (iii) generate a k-th main feature map by applying the convolution operations to the (k-1)-th integrated feature map, the learning method further comprising steps of and or steps of and, the learning device instructing at least one FC layer to generate a detection result of at least one object in the training image by applying at least one FC operation to the n-th main feature map (312, 322, 332); and the learning device outputting one or more loss values by referring to the detection result and its corresponding ground truth, to thereby learn one or more parameters of the CNN by backpropagating the loss values; or the learning device instructing a decoding layer to generate a segmentation result by inputting the n-th main feature map (312, 322, 332) to the decoding layer; and the learning device outputting one or more loss values by referring to the segmentation result and its corresponding ground truth, to thereby learn one or more parameters of the CNN by backpropagating the loss values. (a) a learning device acquiring the training image; and (b) the learning device instructing the 1st convolutional layer to apply the convolution operations to the training image to thereby generate the 1st main feature map and instructing each of the 2nd to the n-th convolutional layers (320,330) to apply operations to the main feature map (312, 322) from its previous convolutional layer to thereby generate each of the 2nd to the n-th main feature maps (312, 322, 332), wherein the learning device instructs a k-th convolutional layer which is one of the h convolutional layers (320, 330) respectively corresponding to the 1st to the h-th feeding layers (410, 420), the h convolutional layers (320, 330) being selected from the 2nd to the n-th convolutional layers (310, 320, 330), to (i) acquire a (k-1)-th main feature map from a (k-1)-th convolutional layer and an m-th sub feature map, which is obtained by resizing the training image into a size of the (k-1)-th main feature map, from an m-th feeding layer corresponding to the k-th convolutional layer, (ii) generate a (k-1)-th integrated feature map by integrating the (k-1)-th main feature map and the m-th sub feature map, and (iii) generate a k-th main feature map by applying the convolution operations to the (k-1)-th integrated feature map, the learning method further comprising steps of and or steps of and, the learning device instructing at least one FC layer to generate a detection result of at least one object in the training image by applying at least one FC operation to the n-th main feature map (312, 322, 332); and the learning device outputting one or more loss values by referring to the detection result and its corresponding ground truth, to thereby learn one or more parameters of the CNN by backpropagating the loss values; or the learning device instructing a decoding layer to generate a segmentation result by inputting the n-th main feature map (312, 322, 332) to the decoding layer; and the learning device outputting one or more loss values by referring to the segmentation result and its corresponding ground truth, to thereby learn one or more parameters of the CNN by backpropagating the loss values.