Applications and Challenges of Deep Learning in Object Detection: A Survey

Abstract

Deep Learning (DL) of Artificial intelligence (AI) is a hot topic in the data science world. It's become crucial as many public and commercial businesses amass huge amounts of domain-specific data, which can provide useful information on issues like fraud detection, national intelligence, cyber security, medical informatics and marketing. Microsoft, Google, Twitter and Amazon Web Services for example, are evaluating very huge amounts of data for business analysis and decisions making, which has an impact on current and future technologies. Through the hierarchical learning process, DL algorithms extract high-level, complex abstractions as data representations. Scene interpretation, video surveillance, robots, and self-driving systems are just a few of the many applications that have prompted an extensive study in the field of computer vision in the last decade. Visual recognition systems, which include picture categorization, localization, and detection, are at the heart of all of these applications and have gathered a lot of research attention. These visual identification algorithms have achieved extraordinary performance, thanks to considerable advancements in neural networks, in particular deep learning. Object tracking and detection are one of the areas where computer vision has had a lot of success. The aim of this paper is to analyze the applications and challenges of DL algorithms for object detection in the last ten (10) years. The state-of-the-art object detection methods, including video tracking, are Applications … Yetunde Josephine OGUNS et al.