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Electronics
Volume 13
Issue 7
10.3390/electronics13071291
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Article

Blasthole Location Detection Using Support Vector Machine and Convolutional Neural Networks on UAV Images and Photogrammetry Models

by
Jorge Valencia
,
Ebrahim Emami
,
Rushikesh Battulwar
,
Ankit Jha
,
Jose A. Gomez
,
Amin Moniri-Morad
and
Javad Sattarvand
*
Department of Mining and Metallurgical Engineering, University of Nevada, 1664 N. Virginia St., Reno, NV 89557, USA
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(7), 1291; https://doi.org/10.3390/electronics13071291
Submission received: 15 February 2024 / Revised: 26 March 2024 / Accepted: 27 March 2024 / Published: 30 March 2024
(This article belongs to the Special Issue Unmanned Aerial Vehicle (UAV)-Based Solutions for 5G and Beyond)
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Abstract

Identifying the as-drilled location of blastholes is crucial for achieving optimal blasting results. This research proposes a novel integrated methodology to control drilling accuracy in open-pit mines. This approach is developed by combining aerial drone images with machine learning techniques. The study investigates the viability of photogrammetry combined with machine learning techniques, particularly Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), for automatically detecting blastholes in photogrammetry representations of blast patterns. To verify the hypothesis that machine learning can detect blastholes in images as effectively as humans, various datasets (drone images) were obtained from different mine sites in Nevada, USA. The images were processed to create photogrammetry mapping of the drill patterns. In this process, thousands of patches were extracted and augmented from the photogrammetry representations. Those patches were then used to train and test different CNN architectures optimized to locate blastholes. After reaching an acceptable level of accuracy during the training process, the model was tested using a piece of completely unknown data (testing dataset). The high recall, precision, and percentage of detected blastholes prove that the combination of SVM, CNN, and photogrammetry (PHG) is an effective methodology for detecting blastholes on photogrammetry maps.
Keywords: blasthole; machine learning; photogrammetry; support vector machine; convolutional neural network; drilling accuracy blasthole; machine learning; photogrammetry; support vector machine; convolutional neural network; drilling accuracy

Share and Cite

MDPI and ACS Style

Valencia, J.; Emami, E.; Battulwar, R.; Jha, A.; Gomez, J.A.; Moniri-Morad, A.; Sattarvand, J. Blasthole Location Detection Using Support Vector Machine and Convolutional Neural Networks on UAV Images and Photogrammetry Models. Electronics 2024, 13, 1291. https://doi.org/10.3390/electronics13071291

AMA Style

Valencia J, Emami E, Battulwar R, Jha A, Gomez JA, Moniri-Morad A, Sattarvand J. Blasthole Location Detection Using Support Vector Machine and Convolutional Neural Networks on UAV Images and Photogrammetry Models. Electronics. 2024; 13(7):1291. https://doi.org/10.3390/electronics13071291

Chicago/Turabian Style

Valencia, Jorge, Ebrahim Emami, Rushikesh Battulwar, Ankit Jha, Jose A. Gomez, Amin Moniri-Morad, and Javad Sattarvand. 2024. "Blasthole Location Detection Using Support Vector Machine and Convolutional Neural Networks on UAV Images and Photogrammetry Models" Electronics 13, no. 7: 1291. https://doi.org/10.3390/electronics13071291

APA Style

Valencia, J., Emami, E., Battulwar, R., Jha, A., Gomez, J. A., Moniri-Morad, A., & Sattarvand, J. (2024). Blasthole Location Detection Using Support Vector Machine and Convolutional Neural Networks on UAV Images and Photogrammetry Models. Electronics, 13(7), 1291. https://doi.org/10.3390/electronics13071291

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