A survey of the application of deep learning in computer vision

Abstract

Deep learning has strong abilities in finding and expressing characteristics of pictures. Recent years, with the arrival of big data era and the development of computers, deep learning has made great breakthroughs and become the focus of the field of computer vision. First the history and classification of deep learning are presented. This thesis also introduces the basic theory of typical deep learning models on computer vision, which include convolutional neural network, recurrent neural network and generative adversarial network. And then summarizing the research situations and progress of deep learning on image classification, image detection, image segmentation as well as video recognition and prediction. Finally, the development and trend of deep learning in the field of computer vision are analyzed. The combination of convolutional neural network and recurrent neural network will be a good choice for video recognition and prediction, which still has a big gap between human beings cognition. And it is the generative adversarial network which has strong ability to generate new samples based on the potential distribution will play an important role in computer vision.