关键词: 目标检测, 移动增强现实, 深度学习, 计算机视觉, 移动边缘计算

Abstract: Mobile augmented reality (AR) is a technology that integrates virtual information with the real world on the
mobile intelligent terminal, therefore the ability to accurately detect the to-be-enhanced objects in the environment
directly determines the performance of mobile AR systems. With the rapid advancement of deep learning, a large
number of deep learning-based methods have been proposed for better detection. However, such problems as limited
computing power, high energy consumption, large model size, and offloading latency make it difficult to combine
deep learning-based object detection with mobile AR. This paper first summarized previous studies on deep
learning-based object detection from both aspects of two stages and one stage, then categorized the object detection
systems for mobile AR, and analyzed the approaches based on local, cloud, or edge ends, as well as collaboration.
Finally, both the advantages and limitations of these methods were summarized, and predictions were made on the problems to be solved and the future development of object detection in mobile AR.

Key words: object detection, mobile augmented reality, deep learning, computer vision, mobile edge computing