A computer implemented method for learning parameters of an object detector based on a CNN, the method comprising the steps of: (a) (i) generating, via one or more convolutional layers, at least one initial feature map by applying one or more convolution operations to a training image, (ii) generating, via an RPN, N proposals corresponding to each of one or more objects in the training image by using the initial feature map, (iii) generating, via a pooling layer, N pooled feature maps, having a width of M1, a height of M2 and a number of channels of J, per each of the N proposals by applying one or more pooling operations to each region, corresponding to each of the N proposals, on the initial feature map, and (iv) generating, via a first transposing layer, an integrated feature map, having a width of N, a height of 1 and a number of channels of M1•M2•J, (b) (i) generating, via a first reshaping layer, a first reshaped feature map having a width of N, a height of H1 and a number of channels of CEIL((M1•M2•J)/H1) by concatenating each of H1 features in each group comprised of each corresponding H1 channels among all channels of the integrated feature map along the height of H1 of the first reshaped feature map, and (ii) generating, via a 1×H1 convolutional layer having a number of filters of K, a first adjusted feature map having a width of N, a height of 1 and a number of channels of K, and (i) generating, via a second reshaping layer, a second reshaped feature map having a width of N, a height of H2 and a number of channels of CEIL(K/H2) by concatenating each of H2 features in each group comprised of each corresponding H2 channels among all channels of the first adjusted feature map along the height of H2 of the second reshaped feature map, and (ii) generating, a 1×H2 convolutional layer having a number of filters of L, a second adjusted feature map having a width of N, a height of 1 and a number of channels of L; and (c) (i) generating, via a second transposing layer, pixel-wise feature maps having a width of 1, a height of 1 and a number of channels of L per each of the N proposals by dividing the second adjusted feature map by each of the pixels, and generating, via a classifying layer, object class information on each of the N proposals by using the pixel-wise feature maps per each of the N proposals, generating, via a detecting layer, object detection information corresponding to the objects in the training image by referring to the object class information and the pixel-wise feature maps per each of the N proposals, calculating, via a detection loss layer, one or more object detection losses by referring to the object detection information and its corresponding GT, and learning at least part of parameters of the 1×H2 convolutional layer, the 1×H1 convolutional layer, and the convolutional layers by backpropagating the object detection losses. (a) (i) generating, via one or more convolutional layers, at least one initial feature map by applying one or more convolution operations to a training image, (ii) generating, via an RPN, N proposals corresponding to each of one or more objects in the training image by using the initial feature map, (iii) generating, via a pooling layer, N pooled feature maps, having a width of M1, a height of M2 and a number of channels of J, per each of the N proposals by applying one or more pooling operations to each region, corresponding to each of the N proposals, on the initial feature map, and (iv) generating, via a first transposing layer, an integrated feature map, having a width of N, a height of 1 and a number of channels of M1•M2•J, (b) (i) generating, via a first reshaping layer, a first reshaped feature map having a width of N, a height of H1 and a number of channels of CEIL((M1•M2•J)/H1) by concatenating each of H1 features in each group comprised of each corresponding H1 channels among all channels of the integrated feature map along the height of H1 of the first reshaped feature map, and (ii) generating, via a 1×H1 convolutional layer having a number of filters of K, a first adjusted feature map having a width of N, a height of 1 and a number of channels of K, and (i) generating, via a second reshaping layer, a second reshaped feature map having a width of N, a height of H2 and a number of channels of CEIL(K/H2) by concatenating each of H2 features in each group comprised of each corresponding H2 channels among all channels of the first adjusted feature map along the height of H2 of the second reshaped feature map, and (ii) generating, a 1×H2 convolutional layer having a number of filters of L, a second adjusted feature map having a width of N, a height of 1 and a number of channels of L; and (c) (i) generating, via a second transposing layer, pixel-wise feature maps having a width of 1, a height of 1 and a number of channels of L per each of the N proposals by dividing the second adjusted feature map by each of the pixels, and generating, via a classifying layer, object class information on each of the N proposals by using the pixel-wise feature maps per each of the N proposals, generating, via a detecting layer, object detection information corresponding to the objects in the training image by referring to the object class information and the pixel-wise feature maps per each of the N proposals, calculating, via a detection loss layer, one or more object detection losses by referring to the object detection information and its corresponding GT, and learning at least part of parameters of the 1×H2 convolutional layer, the 1×H1 convolutional layer, and the convolutional layers by backpropagating the object detection losses.