Applications and Challenges of Deep Learning in Computer Vision

Abstract

Deep learning has gained a lot of prominence in the past few years, with it even taking precedence over other learning techniques quite significantly. The use of computer vision is a very good example of its widespread application. As the amount of data generated becomes more, the complexity of the analysis also increases. This is the ideal application of the Deep Learning Method and it is known to outperform other traditional Machine Learning algorithms by quite some margins as the latter has issues in dealing with high-volume data. The specialty of deep learning is that it is applicable for texts as well as image data alike. Two important algorithms of deep learning that have multiple utilities are Convolutional Neural Network and Deep Belief Network. By using a Convolutional Neural Network, one can extract information from images by detection and recognition. It can be used in the medical science field by locating out tumors accurately and identifying its type and using robots for navigation by locating the hurdles. The main aim of this review paper is to provide a brief about the deep learning methods used. It includes a description of their structure, functioning, and limitation and also includes their utility in computer vision like for object identification, human face, and activity recognition etcetera. In the end, a brief description of the future usage of it and how the newer challenges can be dealt with is shared here.