SoN: Selective Optimal Network for smartphone-based indoor localization in real-time

被引:0
作者
Lee, Kyungsu [1 ]
Lee, Haeyun [2 ]
Hwang, Jae Youn [3 ]
机构
[1] Jeonbuk Natl Univ, Dept Comp Sci & Artificial Intelligence, Jeonju 54896, South Korea
[2] Korea Univ Technol & Educ, Sch Comp Sci & Engn, Cheonan 31253, South Korea
[3] Daegu Gyeongbuk Inst Sci & Technol DGIST, Dept Elect Engn & Comp Sci, Interdisciplinary Studies Artificial Intelligence, Daegu 42988, South Korea
基金
新加坡国家研究基金会;
关键词
Deep learning; Semi-supervised learning; Efficient learning; Scene recognition; Indoor localization; CLASSIFICATION; ARCHITECTURES;
D O I
10.1016/j.eswa.2025.126639
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Deep learning-based scene recognition algorithms have been developed for real-time application in indoor localization systems. However, owing to the slow calculation time resulting from the deep structure of convolutional neural networks, deep learning-based algorithms have limitations in the usage of real-time applications, despite their high accuracy in classification tasks. To significantly reduce the computation time of these algorithms and slightly improve their accuracy, we thus propose a path-selective deep learning network, denoted as Selective Optimal Network (SoN). The SoN selectively uses the depth-variable networks depending on anew indicator, denoted as the classification-complexity of a source image. The SoN reduces the prediction time by selecting optimal depth for the baseline networks corresponding to the input samples. The network was evaluated using two public datasets and two custom datasets for indoor localization and scene classification, respectively. The experimental results indicated that, compared to other deep learning models, the SoN exhibited improved accuracy and enhanced the processing speed by up to 78.59%. Additionally, the SoN was applied to a smartphone-based indoor positioning system in real-time. The results indicated that the SoN shows excellent performance for rapid and accurate classification in real-time applications of indoor localization systems.
引用
收藏
页数:14
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