A learning method for generating integrated object detection information of an integrated image by integrating first object detection information and second object detection information is provided. The method includes steps of: (a) allowing a learning device, if the first object detection information and the second object detection information is acquired, to instruct a concatenating network included in a deep neural network (DNN) to generate pair feature vectors including information on pairs of first original ROIs and second original ROIs; (b) allowing the learning device to instruct a determining network included in the DNN to apply fully connected (FC) operations to the pair feature vectors, to thereby generate (i) determination vectors and (ii) box regression vectors; and (c) allowing the learning device to instruct a loss unit to generate an integrated loss, and perform backpropagation processes by using the integrated loss, to thereby learn at least part of the parameters included in the DNN.
LEARNING METHOD AND LEARNING DEVICE FOR HETEROGENEOUS SENSOR FUSION BY USING MERGING NETWORK WHICH LEARNS NON-MAXIMUM SUPPRESSION