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General Information
    • ISSN: 1793-8236 (Online)
    • Abbreviated Title Int. J. Eng. Technol.
    • Frequency:  Quarterly 
    • DOI: 10.7763/IJET
    • APC: 500 USD
    • Managing Editor: Ms. Isa Yuan 
    • Abstracting/ Indexing:  CNKI,  Google Scholar, Crossref, EBSCO  etc.
    • E-mail: ijet_Editor@126.com
IJET 2024 Vol.16(3): 139-142
doi: 10.7763/IJET.2024.V16.1270

Truck Dumper Control System with Safety Unit Using Real-Time Deep Learning

Nittaya Kerdprasop 1,* , Apirak Worrakantapon 1 , Paradee Chuaybamroong 2 , Kittisak Kerdprasop 1

  • 1 School of Computer Engineering, Suranaree University of Technology, Thailand
  • 2 Department of Environmental Science, Thammasat University, Thailand

* Corresponding author

  • Manuscript received December 30, 2023
  • revised March 4, 2024
  • accepted April 15, 2024
  • published August 9, 2024

Abstract

Presently, industrial factories have moved toward the era of intelligent automation systems, especially, animal feed industries. They contained several automation machines such as a mixer, pellet mill, and truck dumper. The truck dumper has been used to lift the whole truck and dump the raw material into an intake hopper. However, during its process, neighbor areas are identified as a risk area. Thus, if there is a person standing on the truck dumper platform at the time, this may cause fatal injury or death. In this study, a new proposal to develop a model of automatic human detection using a convolutional neural network that learns and recognizes the important characteristics of the target objects has been introduced. The objective of the study was to develop a system of human detection and risk-level identification in the truck dumper control system to prevent accidents. Herein, several experiments have been conducted in order to select the effective and optimal object detection architecture to apply to the truck dumper control system. In the results, the YOLOv4 model outperformed the Faster Region-Convolution Neural Network (R-CNN) model in both precision and processing speed. The average precision was 99.93% on a day-time dataset and 94.25% on a night-time dataset. The overall accuracy of risk identification was 94.18%. An average processing speed was 31.96 frames per second.

Keywords

  • deep learning
  • You Only Look Once (YOLO)
  • truck dumper control system
  • human detection
IJET-V16N3-1270

How to Cite

Copied

Nittaya Kerdprasop, Apirak Worrakantapon, Paradee Chuaybamroong, and Kittisak Kerdprasop, "Truck Dumper Control System with Safety Unit Using Real-Time Deep Learning," International Journal of Engineering and Technology, vol. 16, no. 3, pp. 139-142, 2024. https://doi.org/ 10.7763/IJET.2024.V16.1270

Copyright & License

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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