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DOI: 10.14569/IJACSA.2020.0110204
PDF

Vision-based Indoor Localization Algorithm using Improved ResNet

Author 1: Zeyad Farisi
Author 2: Tian Lianfang
Author 3: Li Xiangyang
Author 4: Zhu Bin

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 2, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The output of the residual network fluctuates greatly with the change of the weight parameters, which greatly affects the performance of the residual network. For dealing with this problem, an improved residual network is proposed. Based on the classical residual network, batch normalization, adaptive -dropout random deactivation function and a new loss function are added into the proposed model. Batch normalization is applied to avoid vanishing/exploding gradients. -dropout is applied to increase the stability of the model, which we select different dropout method adaptively by adjusting parameter. The new loss function is composed by cross entropy loss function and center loss function to enhance the inter class dispersion and intra class aggregation. The proposed model is applied to the indoor positioning of mobile robot in the factory environment. The experimental results show that the algorithm can achieve high indoor positioning accuracy under the premise of small training dataset. In the real-time positioning experiment, the accuracy can reach 95.37.

Keywords: Deep learning; residual network; loss function; dropout; indoor localization

Zeyad Farisi, Tian Lianfang, Li Xiangyang and Zhu Bin, “Vision-based Indoor Localization Algorithm using Improved ResNet” International Journal of Advanced Computer Science and Applications(IJACSA), 11(2), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110204

@article{Farisi2020,
title = {Vision-based Indoor Localization Algorithm using Improved ResNet},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110204},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110204},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {2},
author = {Zeyad Farisi and Tian Lianfang and Li Xiangyang and Zhu Bin}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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