A Systematic Mapping Study of Computer Vision Approaches based on Deep Learning and Neural Network
Eralda Nishani, Betim Çiço · 2017
Deep architectures with convolution structure have been found highly effective and been commonly used in computer vision and image recognition. With the introduction of Graphics Processing Unit (GPU) for general purpose issues, there has been an increasing attention towards exploiting GPU processing power for deep learning algorithms. Also, large amount of data online has made possible to train deep neural networks efficiently and to make networks perform a specific task. This paper aims to perform a systematic mapping study, in order to investigate existing research about implementations of computer vision approaches based on deep learning algorithms and convolutional neural networks. We select a total of 119 papers, which are classified according to field of interest, network type, learning paradigm, research type and contribution type. Our study demonstrates that this field is a promising area for research. We find out that the majority of papers deal with image search approaches (63 %), use Convolutional Neural Networks (CNN) as architecture (65 %), use supervised learning paradigm (55 %) and make use of GPU-acceleration (60 %).