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Deep learning-based object detection in augmented reality: A systematic review
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AI
DOI：10.1016/j.compind.2022.103661.png)
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Abstract
En 中文
Recent advances in augmented reality (AR) and artificial intelligence have caused these technologies to pioneer innovation and alteration in any field and industry. The fast-paced developments in computer vision (CV) and augmented reality facilitated analyzing and understanding the surrounding environments. This paper systematically reviews and presents studies that integrated augmented/mixed reality and deep learning for object detection over the past decade. Five sources including Scopus, Web of Science, IEEE Xplore, ScienceDirect, and ACM were used to collect data. Finally, a total of sixty-nine papers were analyzed from two perspectives: (1) application analysis of deep learning-based object detection in the context of augmented reality and (2) analyzing the use of servers or local AR devices to perform the object detection computations to understand the relation between object detection algorithms and AR technology. Furthermore, the advantages of using deep learning-based object detection to solve the AR problems and limitations hindering the ultimate use of this technology are critically discussed. Our findings affirm the promising future of integrating AR and CV. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Deep learning
Object detection
Augmented reality
Mixed reality
Computation platform
Systematic review
Journal
IF:
9.1
Papers:
2.9K
Citations:
1.1W
