PROCÉDÉ D'APPRENTISSAGE ET DISPOSITIF D'APPRENTISSAGE D'EXTRACTION DE CARACTÉRISTIQUE À PARTIR D'UNE IMAGE D'ENTRÉE À L'AIDE DE COUCHES À CONVOLUTION DANS DES BLOCS MULTIPLES DANS UN CNN, DONNANT LIEU À UNE OPTIMISATION DU MATÉRIEL PERMETTANT DE SATISFAIRE UN INDICE DE PERFORMANCE CLÉ ET PROCÉDÉ DE TEST ET DISPOSITIF DE TEST LES UTILISANT

摘要

A learning method for extracting features from an input image by hardware optimization using n blocks in a convolutional neural network (CNN) is provided. The method includes steps of: a learning device instructing a first convolutional layer of a k-th block to elementwise add a (1_1)-st to a (k_1)-st feature maps or their processed feature maps, and instructing a second convolutional layer of the k-th block to generate a (k_2)-nd feature map; and feeding a pooled feature map, generated by pooling an ROI area on an (n_2)-nd feature map or its processed feature map, into a feature classifier; and instructing a loss layer to calculate losses by referring to outputs of the feature classifier and their corresponding GT. By optimizing hardware, CNN throughput can be improved, and the method becomes more appropriate for compact networks, mobile devices, and the like. Further, the method allows key performance index to be satisfied.