Review of real-time deep learning-based object detection for mobile augmented reality

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.