PROCÉDÉ ET DISPOSITIF D'APPRENTISSAGE POUR DÉTECTEUR D'OBJETS BASÉS SUR CNN À UTILISER POUR UNE SURVEILLANCE PANORAMIQUE OU À CAMÉRAS MULTIPLES UTILISANT LA CONCATÉNATION D'IMAGES ET UN RÉSEAU DE FUSION D'OBJETS CIBLES ET PROCÉDÉ ET DISPOSITIF DE TEST LES UTILISANT

摘要

A method for learning parameters of an object detector based on a CNN adaptable to customers' requirements such as KPI by using an image concatenation and a target object merging 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 steps of: a learning device instructing an image-manipulating network to generate n manipulated images; instructing an RPN to generate first to n-th object proposals respectively in the manipulated images, and instructing an FC layer to generate first to n-th object detection information; and instructing the target object merging network to merge the object proposals and merge the object detection information. In this method, the object proposals can be generated by using lidar. The method can be useful for multi-camera, SVM(surround view monitor), and the like, as accuracy of 2D bounding boxes improves.