Safer Stack: Safe Dump of Off-Highways Trucks in Slope Crest Windrows

Authors

  • Lorrainy Rembiski Delfino Act Digital https://orcid.org/0000-0002-8335-1599
  • Marina Cunha Galvão de França Vale https://orcid.org/0009-0004-9529-1724
  • Jaquelini Kumm Vale
  • Marcus Ventura Vale
  • Gabriel Flausino de Souza Act Digital
  • Allan Lorenzoni Canal Act Digital
  • Yargo Alves Sampaio Act Digital
  • Fabiana Zambroni Neves Vale

DOI:

https://doi.org/10.22456/2175-2745.143633

Keywords:

Computer Vision, Image Recognition, Dumping Trucks, Safety

Abstract

During operation, the dump truck sometimes needs to operate close to a slope crest windrow. This article aims to present the experimentation of a system, called Safer Stack, to assist the dump truck operator when reversing in front of a slope crest windrow, with the purpose of informing him the safe distance to perform the dump and generating alerts when there is a risk. The system includes computer vision, through image recognition, and distance measurement, through a LiDAR. The information will be used in a graphical interface, with visual alerts and audible alarms for the dump truck operator. Based on the tests carried out, it was confirmed that the combination of technologies in a final solution, since it presented 98% accuracy in the trained scenarios, has the potential to generate highly efficient results and make the operation safer.

Downloads

Download data is not yet available.

References

BELLANCA, J. L. et al. Why do haul truck fatal accidents keep occurring? Mining, Metallurgy & Exploration, v. 38, n. 2, p. 1019–1029, 2021.

RUFF, T. Evaluation of a radar-based proximity warning system for off-highway dump trucks. Accident Analysis & Prevention, v. 38, n. 1, p. 92–98, 2006. ISSN 0001-4575. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S0001457505001259⟩.

SUN, E.; NIETO, A.; LI, Z. GPS and Google Earth based 3D assisted driving system for trucks in surface mines. Mining Science and Technology (China), v. 20, n. 1, p. 138–142, 2010. ISSN 1674-5264. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S1674526409601757⟩.

YU, J. et al. Development of a heavy truck reversing safety system based on pedestrian detection and tracking using binocular vision stitching. In: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023). [S.l.: s.n.], 2023. v. 12707, p. 1270702.

MONIRI-MORAD, A. et al. Powered haulage safety, challenges, analysis, and solutions in the mining industry; a comprehensive review. Results in Engineering, v. 21, p. 101684, 2024. ISSN 2590-1230. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S2590123023008113⟩.

Mine Safety and Health Administration. Fatality Reports. Disponível em: ⟨https://www.msha.gov/data-and-reports/fatality-reports/search⟩.

EURO1. 5 Common Causes of Dump Truck Accidents and How to Avoid Them. Disponível em: ⟨https://www.euro1training.com/news/common-causes-dump-truck-accidents-avoid/⟩.

GARCÍA-GONZÁLEZ, J. et al. Vehicle overtaking hazard detection over onboard cameras using deep convolutional networks. In: Department of Computer Languages and Computer Science. University of Málaga. [S.l.: s.n.], 2022.

SCHMIDHUBER, J. Deep learning in neural networks: an overview. Neural Networks, v. 61, p. 85–117, 2015. ISSN 0893-6080. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S0893608014002135⟩.

LIU, Y. et al. A review of deep learning in image classification for mineral exploration. Minerals Engineering, v. 204, p. 108433, 2023. ISSN 0892-6875. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S0892687523004478⟩.

JUNIOR, E. C. V. Métricas de desempenho: classificação. Disponível em: ⟨https://geam.paginas.ufsc.br/files/2020/02/Metricas-desempenho-classificacao2-1.pdf⟩.

Downloads

Published

2025-02-20

How to Cite

Rembiski Delfino, L., Cunha Galvão de França, M., Kumm, J., Ventura, M., Flausino de Souza, G., Lorenzoni Canal, A., Alves Sampaio, Y., & Zambroni Neves, F. (2025). Safer Stack: Safe Dump of Off-Highways Trucks in Slope Crest Windrows. Revista De Informática Teórica E Aplicada, 32(1), 143–150. https://doi.org/10.22456/2175-2745.143633

Issue

Section

WVC2024