Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
A machine vision-based approach to measuring the size distribution of rocks on a conveyor belt
This work involves the development of a vision-based system for measuring the size distribution of rocks on a conveyor belt. The system has applications in automatic control and optimization of milling machines, and the selection of optimal blasting methods in the mining industry. Rock size is initi...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Electrical Engineering
2024
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613729420476416 |
|---|---|
| access_status_str | Open Access |
| author | Mkwelo, Simphiwe |
| author2 | de Jager, Gerhard |
| author_browse | Mkwelo, Simphiwe de Jager, Gerhard |
| author_facet | de Jager, Gerhard Mkwelo, Simphiwe |
| author_sort | Mkwelo, Simphiwe |
| collection | Thesis |
| description | This work involves the development of a vision-based system for measuring the size distribution of rocks on a conveyor belt. The system has applications in automatic control and optimization of milling machines, and the selection of optimal blasting methods in the mining industry. Rock size is initially assumed to be the projected rock surface area due to the constraint imposed by the 2D nature of images. This measurement is facilitated by locating connected rock-edge pixels. Rock edge detection is achieved using a watershed-based segmentation process. This process involves image pre-filtering with edge preserving filters at various degrees of filtering. The output of each filtering stage is retained and marker-driven watersheds are applied on each output resulting to traces of detected rock boundaries. Watershed boundary selection is then applied to select boundaries which are most likely to be rock edges based on rock features. Finally, rock recognition using feature classification is applied to remove non-rock watershed boundaries. The projected rock area distribution of a test-set is measured and compared to corresponding projected areas of manually segmented images. The obtained distributions are found to be similar with an RMS error of 2.37% on the test-set. Finally, sieve data is collected in the form of actual rock size distributions and a quantitative comparison between the actual and machine measured distributions is performed. The overall quantitative result is that the two rock size distributions are significantly different. However, after incorporating a stereology-based correction, hypothesis tests on a 3m belt-cut test-set show that the obtained distributions are similar. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40138 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:40:46.716Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40138 A machine vision-based approach to measuring the size distribution of rocks on a conveyor belt Mkwelo, Simphiwe de Jager, Gerhard Electrical Engineering This work involves the development of a vision-based system for measuring the size distribution of rocks on a conveyor belt. The system has applications in automatic control and optimization of milling machines, and the selection of optimal blasting methods in the mining industry. Rock size is initially assumed to be the projected rock surface area due to the constraint imposed by the 2D nature of images. This measurement is facilitated by locating connected rock-edge pixels. Rock edge detection is achieved using a watershed-based segmentation process. This process involves image pre-filtering with edge preserving filters at various degrees of filtering. The output of each filtering stage is retained and marker-driven watersheds are applied on each output resulting to traces of detected rock boundaries. Watershed boundary selection is then applied to select boundaries which are most likely to be rock edges based on rock features. Finally, rock recognition using feature classification is applied to remove non-rock watershed boundaries. The projected rock area distribution of a test-set is measured and compared to corresponding projected areas of manually segmented images. The obtained distributions are found to be similar with an RMS error of 2.37% on the test-set. Finally, sieve data is collected in the form of actual rock size distributions and a quantitative comparison between the actual and machine measured distributions is performed. The overall quantitative result is that the two rock size distributions are significantly different. However, after incorporating a stereology-based correction, hypothesis tests on a 3m belt-cut test-set show that the obtained distributions are similar. 2024-07-02T09:52:17Z 2024-07-02T09:52:17Z 2004 2024-06-25T13:49:33Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/40138 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Mkwelo, Simphiwe A machine vision-based approach to measuring the size distribution of rocks on a conveyor belt |
| thesis_degree_str | Master's |
| title | A machine vision-based approach to measuring the size distribution of rocks on a conveyor belt |
| title_full | A machine vision-based approach to measuring the size distribution of rocks on a conveyor belt |
| title_fullStr | A machine vision-based approach to measuring the size distribution of rocks on a conveyor belt |
| title_full_unstemmed | A machine vision-based approach to measuring the size distribution of rocks on a conveyor belt |
| title_short | A machine vision-based approach to measuring the size distribution of rocks on a conveyor belt |
| title_sort | machine vision based approach to measuring the size distribution of rocks on a conveyor belt |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/40138 |
| work_keys_str_mv | AT mkwelosimphiwe amachinevisionbasedapproachtomeasuringthesizedistributionofrocksonaconveyorbelt AT mkwelosimphiwe machinevisionbasedapproachtomeasuringthesizedistributionofrocksonaconveyorbelt |