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Node quality control using drones and object detection

Authors: Stephenson, Jason; Strong, Shaun;

Node quality control using drones and object detection

Abstract

Wireless nodal surveys are now commonplace in Australian land seismic. It is essential that rigorous QC of the condition of the nodes is performed during these surveys. Acquiring footage using off-the-shelf drones can assist with this. Analysis of drone footage using open-source machine learning algorithms can reduce the amount of human analysis hours required. The best results were obtained by using a combination of OpenCV blob detection, and the Tensor Flow Convolutional Neural Networks.

Open-Access Online Publication: March 03, 2023

Keywords

UAV, machine-learning, seismic, computer vision.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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