Title page for ETD etd-05202013-124438

Type of Document Master's Thesis
Author Christie, Gordon A
URN etd-05202013-124438
Title Computer Vision for Quarry Applications
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Kevin Kochersberger Committee Chair
A. Lynn Abbott Committee Member
Devi Parikh Committee Member
Erik Westman Committee Member
  • Computer Vision
  • Rocks
  • Image-based 3D Reconstruction Real-time Image Proc
  • Image Segmentation
Date of Defense 2013-05-06
Availability restricted
This thesis explores the use of computer vision to facilitate three di erent processes of

a quarry's operation. The rst is the blasting process. This is where operators determine

where to drill in order to execute an ecient and safe blast. Having an operator manually

determine the drilling angles and positions can lead to inecient and dangerous blasts. By

using two cameras, oriented vertically, and separated by a xed baseline, Structure from

Motion techniques can be used to create a scaled 3D model of a bench. This can then be

analyzed to provide operators with borehole locations and drilling angles in relation to xed

reference targets.

The second process explored is the crushing process, where the rocks pass through

di erent crushers that reduce the rocks into smaller sizes. The crushed rocks are then

dropped onto a moving conveyor belt. The maximum dimension of the rocks exiting the

crushers should not exceed size thresholds that are speci c to each crusher. This thesis

presents a 2D vision system capable of estimating the size distribution of the rocks by

attempting to segment the rocks in each image. The size distribution, based on the maximum

dimension of each rock, is estimated by nding the maximum dimension in the image in pixels

and converting that to inches.

The third process of the quarry operations explored is where the nal product is piled up

to form stockpiles. For inventory purposes, operators often carry out a manual estimation of

the size of a the stockpile. This thesis presents a vision system capable of providing a more

accurate estimate for the size of the stockpile by using Structure from Motion techniques to

create a 3D reconstruction. User interaction helps to nd the points that are relevant to the

stockpile in the resulting point cloud, which are then used to estimate the volume.

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