01whole.pdf (10.08 MB)
Download file

Machine vision simplified with distance and ranging sensors

Download (10.08 MB)
thesis
posted on 28.03.2022, 19:44 by Ryan George Barnes
Machine vision in industry is dominated by 2D cameras. These camera systems are very effective for object tracking, but are labour and skill intensive to implement and require powerful standalone controllers to process the images. 3D and 2D ranging distance sensors provide three-dimensional data which could easily be utilised to perform the same tasks, without the issues of ambient light changes which cripple a 2D camera's ability to function. Currently distance and ranging sensors are marginalised to quality control applications in the machine vision field, focusing on product fill completeness and profile consistency checking. These distance-based sensors have the potential to perform tasks currently done by 2D cameras in industrial vision application, but in a fundamentally different way. The sensors provide an enhanced way of looking at a scene that would be very useful in applications which require identification of shapes and object tracking. Implemented correctly, the ranging and distance sensors should provide better and more flexible performance. The ability for these sensors to detect the actual size and distance of these objects eliminates the need for estimating size and position, which currently requires an experienced programmer to teach the system what it is incapable of learning itself. Furthermore, if programmed correctly, a distance-sensor-based tracking system could be made far easier to implement than traditional cameras and therefore cheaper on labour and more accessible to less experienced users and companies. This thesis project will quantitatively compare the accuracy of multiple industrial distance and ranging sensors on a range of commercial consumer goods. It will also develop a program to automate the tests and feature a quick setup program to explore the viability of end-user self-installation.

History

Table of Contents

1. Introduction -- 2. Literature review -- 3. Project methodology -- 4. Results and analysis -- 5. Time-line -- 6. Conclusions and future work -- Appendix -- References.

Notes

Empirical thesis. Bibliography: page 51

Awarding Institution

Macquarie University

Degree Type

Thesis bachelor honours

Degree

BSc (Hons), Macquarie University, Faculty of Science and Engineering, School of Engineering

Department, Centre or School

School of Engineering

Year of Award

2016

Principal Supervisor

David Inglis

Additional Supervisor 1

Rodney Barnes

Rights

Copyright Ryan George Barnes 2016. Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (xiii, 51 pages colour illustrations)

Former Identifiers

mq:70325 http://hdl.handle.net/1959.14/1262577

Usage metrics

Keywords

Exports