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Visual localisation of electricity pylons for power line inspection
Inspection of power infrastructure is a regular maintenance event. To date the inspection process has mostly been done manually, but there is growing interest in automating the process. The automation of the inspection process will require an accurate means for the localisation of the power infrastr...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2023
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| _version_ | 1867613835556290560 |
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| access_status_str | Open Access |
| author | Ali, Emmanuel Yahi |
| author2 | Nicolls, Frederick |
| author_browse | Ali, Emmanuel Yahi Nicolls, Frederick |
| author_facet | Nicolls, Frederick Ali, Emmanuel Yahi |
| author_sort | Ali, Emmanuel Yahi |
| collection | Thesis |
| description | Inspection of power infrastructure is a regular maintenance event. To date the inspection process has mostly been done manually, but there is growing interest in automating the process. The automation of the inspection process will require an accurate means for the localisation of the power infrastructure components. In this research, we studied the visual localisation of a pylon. The pylon is the most prominent component of the power infrastructure and can provide a context for the inspection of the other components. Point-based descriptors tend to perform poorly on texture less objects such as pylons, therefore we explored the localisation using convolutional neural networks and geometric constraints. The crossings of the pylon, or vertices, are salient points on the pylon. These vertices aid with recognition and pose estimation of the pylon. We were successfully able to use a convolutional neural network for the detection of the vertices. A model-based technique, geometric hashing, was used to establish the correspondence between the stored pylon model and the scene object. We showed the effectiveness of the method as a voting technique to determine the pose estimation from a single image. In a localisation framework, the method serves as the initialization of the tracking process. We were able to incorporate an extended Kalman filter for subsequent incremental tracking of the camera relative to the pylon. Also, we demonstrated an alternative tracking using heatmap details from the vertex detection. We successfully demonstrated the proposed algorithms and evaluated their effectiveness using a model pylon we built in the laboratory. Furthermore, we revalidated the results on a real-world outdoor electricity pylon. Our experiments illustrate that model-based techniques can be deployed as part of the navigation aspect of a robot. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/38459 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:42:27.935Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| 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:11427/38459 Visual localisation of electricity pylons for power line inspection Ali, Emmanuel Yahi Nicolls, Frederick Electricity Pylons Inspection of power infrastructure is a regular maintenance event. To date the inspection process has mostly been done manually, but there is growing interest in automating the process. The automation of the inspection process will require an accurate means for the localisation of the power infrastructure components. In this research, we studied the visual localisation of a pylon. The pylon is the most prominent component of the power infrastructure and can provide a context for the inspection of the other components. Point-based descriptors tend to perform poorly on texture less objects such as pylons, therefore we explored the localisation using convolutional neural networks and geometric constraints. The crossings of the pylon, or vertices, are salient points on the pylon. These vertices aid with recognition and pose estimation of the pylon. We were successfully able to use a convolutional neural network for the detection of the vertices. A model-based technique, geometric hashing, was used to establish the correspondence between the stored pylon model and the scene object. We showed the effectiveness of the method as a voting technique to determine the pose estimation from a single image. In a localisation framework, the method serves as the initialization of the tracking process. We were able to incorporate an extended Kalman filter for subsequent incremental tracking of the camera relative to the pylon. Also, we demonstrated an alternative tracking using heatmap details from the vertex detection. We successfully demonstrated the proposed algorithms and evaluated their effectiveness using a model pylon we built in the laboratory. Furthermore, we revalidated the results on a real-world outdoor electricity pylon. Our experiments illustrate that model-based techniques can be deployed as part of the navigation aspect of a robot. 2023-09-08T09:51:43Z 2023-09-08T09:51:43Z 2023 2023-09-08T09:51:02Z Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/38459 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment |
| spellingShingle | Electricity Pylons Ali, Emmanuel Yahi Visual localisation of electricity pylons for power line inspection |
| thesis_degree_str | Doctoral |
| title | Visual localisation of electricity pylons for power line inspection |
| title_full | Visual localisation of electricity pylons for power line inspection |
| title_fullStr | Visual localisation of electricity pylons for power line inspection |
| title_full_unstemmed | Visual localisation of electricity pylons for power line inspection |
| title_short | Visual localisation of electricity pylons for power line inspection |
| title_sort | visual localisation of electricity pylons for power line inspection |
| topic | Electricity Pylons |
| url | http://hdl.handle.net/11427/38459 |
| work_keys_str_mv | AT aliemmanuelyahi visuallocalisationofelectricitypylonsforpowerlineinspection |