Railway Catenary systems play a crucial role in the safe and reliable transportation of goods and people throughout the world. Monitoring the catenary infrastructure is crucial for safety purposes and therefore requires inspections. However, the current inspection methods are not sufficient for detecting all possible failure modes. The use of lidar has been proposed to augment the current inspection methods. This research proposes two methods for the classification of various overhead catenary components, resulting from lidar data, both solely relying on the coordinates of the captured datapoints. The methods resulted from a literature analysis and the parameters were obtained trough experimentation with a small dataset. The methods were validated using a larger dataset of 22.5 km between Boden and Gällivare and achieved promising outcomes. The first method resulted in an F1 score of 93,37% was obtained with 87,39% accuracy, whereas the second method, using a simple morphological region filtered obtain an F1 score of 95,48% and an accuracy of 91,27%. The novel contributions of the processing of lidar data in railway infrastructure is the use of a simple morphological region filter and the use of surface variation, a geometric feature for the extraction of masts and bridges. Further research is advised into the computational efficiency and further classification of components in the overhead catenary system.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-92665 |
| Date | January 2022 |
| Creators | Voorwald, Daniël |
| Publisher | Luleå tekniska universitet, Drift, underhåll och akustik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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