A method for learning a convolutional neural network (CNN)-based object detector, comprising steps of: (a) if a training image including at least one object is inputted, instructing, by a learning device , at least one convolutional layer to apply at least one convolution operation to the training image, to thereby output at least one feature map and instructing a region proposal network, RPN, to output k regions of interest, ROIs, in the training image by using the feature map; (b) (i) instructing, by the learning device , each of a plurality of pooling layers having each different pooling scale to pool each of areas, corresponding to each of the k ROIs, on the feature map per each of their scales, to thereby output each of k first feature vectors corresponding to each of the k ROIs per each of their scales, (ii) instructing, by the learning device , each of first fully connected, FC, layers corresponding to each of the pooling layers to output each of k second feature vectors per each of their scales by using each of the k first feature vectors per each of their scales, and (iii) instructing, by the learning device , one or more second FC layers to output each of class information and each of regression information corresponding to each of the k ROIs by using each of the k second feature vectors per each of their scales; and (c) instructing, by the learning device , a first loss layer to generate class losses and regression losses corresponding to the k ROIs by referring to each of the class information, each of the regression information, and their corresponding first ground truths, GTs, of the training image to thereby learn the second FC layers , the first FC layers , and the convolutional layer through backpropagation by using the class losses and the regression losses, wherein the first GTs of the training image are class information and regression information indicating information on the location of the bounding box of the at least one object of the training image. (a) if a training image including at least one object is inputted, instructing, by a learning device , at least one convolutional layer to apply at least one convolution operation to the training image, to thereby output at least one feature map and instructing a region proposal network, RPN, to output k regions of interest, ROIs, in the training image by using the feature map; (b) (i) instructing, by the learning device , each of a plurality of pooling layers having each different pooling scale to pool each of areas, corresponding to each of the k ROIs, on the feature map per each of their scales, to thereby output each of k first feature vectors corresponding to each of the k ROIs per each of their scales, (ii) instructing, by the learning device , each of first fully connected, FC, layers corresponding to each of the pooling layers to output each of k second feature vectors per each of their scales by using each of the k first feature vectors per each of their scales, and (iii) instructing, by the learning device , one or more second FC layers to output each of class information and each of regression information corresponding to each of the k ROIs by using each of the k second feature vectors per each of their scales; and (c) instructing, by the learning device , a first loss layer to generate class losses and regression losses corresponding to the k ROIs by referring to each of the class information, each of the regression information, and their corresponding first ground truths, GTs, of the training image to thereby learn the second FC layers , the first FC layers , and the convolutional layer through backpropagation by using the class losses and the regression losses, <EOS>