A method for learning parameters of an object detector based on a CNN adaptable to customers′ requirements such as KPI by using a target object merging network and a target region estimating network is provided. The CNN can be redesigned when scales of objects change as a focal length or a resolution changes depending on the KPI. The method includes the steps of allowing a learning device to (i) instruct the target region estimating network to search for k-th estimated target regions, (ii) instruct an RPN to generate (k_1)^th to (k_n)^th object proposals, corresponding to an object on a (k_1)^th to a (k_n)^th manipulated images, and (iii) instruct the target object merging network to merge the object proposals and merge (k_1)^th to (k_n)^th object detection information, outputted from an FC layer. The method can be useful for multi-camera, SVM (surround view monitor), and the like, as accuracy of 2D bounding boxes increases.
CNN LEARNING METHOD AND LEARNING DEVICE FOR OBJECT DETECTOR BASED ON CNN ADAPTABLE TO CUSTOMERS' REQUIREMENTS SUCH AS KEY PERFORMANCE INDEX USING TARGET OBJECT MERGING NETWORK AND TARGET REGION ESTIMATING NETWORK AND TESTING METHOD AND TESTING DEVICE USING THE SAME TO BE USED FOR MULTI-CAMERA OR SURROUND VIEW MONITORING