In this article, we give a comprehensive overview of recent methods in object detection using deep learning and their uses in augmented reality. The objective is to present a complete understanding of these algorithms and how augmented reality functions and services can be improved by integrating these methods. We discuss in detail the different characteristics of each approach and their influence on real-time detection performance. Experimental analyses are provided to compare the performance of each method and make meaningful conclusions for their use in augmented reality. Two-stage detectors generally provide better detection performance, while single-stage detectors are significantly more time efficient and more applicable to real-time object detection. Finally, we discuss several future directions to facilitate and stimulate future research on object detection in augmented reality. Keywords: object detection, deep learning, convolutional neural network, augmented reality.