A learning method for adjusting one or more parameters of a convolutional neural network by using loss augmentation, wherein the using loss augmentation comprises utilizing an additional ground truth region of interest, comprising steps of: (a) acquiring at least one feature map from a training image by a learning device; (b) performing (i) a process of acquiring one or more proposal region of interests (ROIs) corresponding to at least one object on the training image by using a region proposal network and a process of creating at least one first pooled feature map by applying pooling operation to areas, on the feature map, corresponding to the proposal ROIs, and (ii) a process of acquiring the ground truth ROI, on the training image, corresponding to the object and a process of creating a second pooled feature map by applying pooling operation to an area, on the feature map, corresponding to the GT ROI by the learning device; and (c) performing (i) a process of acquiring information on pixel data of a first bounding box by regression when the first pooled feature map and the second pooled feature map are inputted into a first fully connected layer and allowing the first FC layer to confirm a class of the object by applying classification operations, (ii) a process of acquiring a first comparative data by a comparison between the information on the pixel data of the first bounding box and information on pixel data of a GT bounding box which is preferably acquired by a human, and (iii) a process of backpropagating information on the first comparative data to thereby perform a first adjustment of at least part of the parameters by the learning device, wherein, at the step of (c), the learning device performs (i) a process of acquiring information on pixel data of a second bounding box by a regression when the first pooled feature map is inputted into a second fully connected layer, (ii) a process of acquiring a second comparative data by a comparison between the information on the pixel data of the second bounding box and the information on the pixel data of the GT bounding box, and (iii) a process of backpropagating information on the second comparative data to thereby perform a second adjustment of at least part of the parameters, wherein the GT ROI is an area identical to the GT bounding box which is smaller than the training image and selected to include the object on the training image by a human. (a) acquiring at least one feature map from a training image by a learning device; (b) performing (i) a process of acquiring one or more proposal region of interests (ROIs) corresponding to at least one object on the training image by using a region proposal network and a process of creating at least one first pooled feature map by applying pooling operation to areas, on the feature map, corresponding to the proposal ROIs, and (ii) a process of acquiring the ground truth ROI, on the training image, corresponding to the object and a process of creating a second pooled feature map by applying pooling operation to an area, on the feature map, corresponding to the GT ROI by the learning device; and (c) performing (i) a process of acquiring information on pixel data of a first bounding box by regression when the first pooled feature map and the second pooled feature map are inputted into a first fully connected layer and allowing the first FC layer to confirm a class of the object by applying classification operations, (ii) a process of acquiring a first comparative data by a comparison between the information on the pixel data of the first bounding box and information on pixel data of a GT bounding box which is preferably acquired by a human, and (iii) a process of backpropagating information on the first comparative data to thereby perform a first adjustment of at least part of the parameters by the learning device, wherein, at the step of (c), the learning device performs (i) a process of acquiring information on pixel data of a second bounding box by a regression when the first pooled feature map is inputted into a second fully connected layer, (ii) a process of acquiring a second comparative data by a comparison between the information on the pixel data of the second bounding box and the information on the pixel data of the GT bounding box, and (iii) a process of backpropagating information on the second comparative data to thereby perform a second adjustment of at least part of the parameters, wherein the GT ROI is an area identical to the GT bounding box which is smaller than the training image and selected to include the object on the training image by a human.