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Exploring explainability and robustness of point cloud segmentation deep learning model by visualization

Verburg, F.M. (2022) Exploring explainability and robustness of point cloud segmentation deep learning model by visualization.

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Abstract:Modern deep learning techniques are very suitable for point cloud segmentation of catenary arches of railway systems. The downside of deep learning models is that they have a low explainability. In this paper we explore the explainability of a PointNet++ model that is used for segmenting point clouds of catenary arches. The exploring of explainability is done by creating a pipeline for adapting, segmenting and visualizing the point clouds. By adapting the point clouds the robustness of the model is also tested. From this research it follows that the PointNet++ model mainly relies on the location of the objects in order to segment them. Changing the shape of an object does not have a significant impact on the performance of the model.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
Link to this item:https://purl.utwente.nl/essays/89440
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