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Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
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Deep Learning Scene Recognition Method Based on Localization Enhancement.

Wei Guo1, Ran Wu2, Yanhua Chen3

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China. guowei-lmars@whu.edu.cn.

Sensors (Basel, Switzerland)
|October 13, 2018
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Summary
This summary is machine-generated.

This study introduces a novel convolutional neural network (CNN) model for accurate indoor localization. By combining image data and signals of opportunity, the model significantly improves location prediction accuracy.

Keywords:
deep learningindoor positioningscene recognitionsignals of opportunity

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geomatics Engineering

Background:

  • Indoor localization is crucial for many applications.
  • Signals of opportunity (e.g., Wi-Fi, cellular) are increasingly used for localization.
  • Mobile devices can capture both environmental images and signals of opportunity.

Purpose of the Study:

  • To design a convolutional neural network (CNN) model for indoor localization.
  • To integrate image features and signals of opportunity for enhanced accuracy.
  • To improve indoor scene recognition and reduce localization errors.

Main Methods:

  • A CNN model was designed to concatenate features from image data and signals of opportunity.
  • Transfer learning was applied using the Inception V3 network for feature extraction and scene recognition.
  • Indoor scene datasets were used to simulate and evaluate indoor location probability.

Main Results:

  • The proposed model achieved prediction accuracies of 97.0% and 96.6% in two different experimental scenarios.
  • Compared to overlapping positioning and base map methods (69.0%, 81.2%), the proposed model showed significant improvement.
  • Compared to a fine-tuned Inception V3 model (73.3%, 77.7%), the proposed model demonstrated higher accuracy.
  • Indoor scene recognition accuracy was improved, with reduced errors at scene transitions and increased recognition of similar scenes.

Conclusions:

  • The proposed CNN model effectively integrates image and signals of opportunity for superior indoor localization.
  • The method enhances indoor scene recognition, particularly at the spatial connections of different scenes.
  • This approach offers a reliable and convenient solution for precise indoor positioning.