Substation Danger Sign Detection and Recognition using Convolutional Neural Networks

Authors

  • Wajid Ali School of Automation and Electrical Engineering, Lanzhou Jiaotong University, China
  • Guo Wang School of Automation and Electrical Engineering, Lanzhou Jiaotong University, China
  • Kalim Ullah University of Science and Technology Bannu, Pakistan
  • Muhammad Salman School of Automation and Electrical Engineering, Lanzhou Jiaotong University, China
  • Sajad Ali School of Automation and Electrical Engineering, Lanzhou Jiaotong University, China
Volume: 13 | Issue: 1 | Pages: 10051-10059 | February 2023 | https://doi.org/10.48084/etasr.5476

Received: 14 November 2022 | Revised: 7 December 2022 | Accepted: 15 December 2022 | Online: 5 February 2023


Corresponding author: Guo Wang

Abstract

This paper focuses on the training of a deep neural network regarding danger sign detection and recognition in a substation. It involved applying the concepts of neural networks and computer vision to achieve results similar to traffic sign and number plate detection systems. The input data were captured in three distinct formats, i.e. grayscale, RGB, and YCbCr, which have been used as a base for comparison in this paper. The efficiency of the neural network was tested on a unique data set involving danger signs present in industrial and processing facilities. The data set was unique, consisting of four distinct symbols. The trained data were selected so that they would not facilitate overfitting and also would not be under fitted. The accuracy of the model varied with the input type and was tested with two distinct classifiers, CNN and SVM, and the results were compared. The model was designed to be fast and accurate, and it can be implemented on mobile devices.

Keywords:

CNN, Neural Network, comparative study, danger sign detection

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How to Cite

[1]
W. Ali, G. Wang, K. Ullah, M. Salman, and S. Ali, “Substation Danger Sign Detection and Recognition using Convolutional Neural Networks”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 1, pp. 10051–10059, Feb. 2023.

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Copyright (c) 2022 Wajid Ali, Guo Wang, Kalim Ullah, Muhammad Salman, Sajad Ali

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