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Target location detection of mobile robots based on R-FCN deep convolutional neural network

Author

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  • Hua Cen

    (Guangxi Modern Polytechnic College)

Abstract

In order to improve the target location detection effect of mobile robots, this paper combines convolutional neural network and recurrent neural network to construct a model for solving abnormal sound event detection. Moreover, this paper constructs a convolutional neural network architecture suitable for feature extraction of audio signals, uses the recurrent neural network to classify each frame of audio signals, and applies the improved R-FCN deep convolutional neural network to the target location detection of mobile robots. In addition, this article uses Matlab to carry out system simulation construction, and design and use the system to carry out performance verification. Through experimental research, it can be seen that the target location system of mobile robot based on R-FCN deep convolutional neural network constructed in this paper can effectively improve the location speed and location accuracy compared with traditional location systems.

Suggested Citation

  • Hua Cen, 2023. "Target location detection of mobile robots based on R-FCN deep convolutional neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(2), pages 728-737, April.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:2:d:10.1007_s13198-021-01514-z
    DOI: 10.1007/s13198-021-01514-z
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