| dc.description.abstract | Deep learning and computer vision are two thriving research areas within machine learning. In recent years, as the available computing power has grown, it has led to the possibility of combining the approaches, achieving state-of-the-art results. An area of research that has greatly benefited from this development is building detection. Although the algorithms produce satisfactory results, there are still many limitations. One significant problem is the quality and edge sharpness of the segmentation masks, which are not up to the standard required by the mapping industry. The predicted mask boundaries need to be sharper and more precise to have practical use in map production.
This thesis introduces a novel Data Enhancement Technique (DET) to improve the boundary quality of segmentation masks. DET has two approaches, Seg-DET, which uses a segmentation network, and Edge-DET, which uses an edge-detection network. Both techniques highlight buildings, creating a better input foundation for a secondary segmentation model. Additionally, we introduce ABL(RMI), a new compounding loss consisting of Region Mutual Information Loss (RMI), Lovasz-Softmax Loss (Lovasz), and Active Boundary Loss (ABL). The combination of loss functions in ABL(RMI) is optimized to enhance and improve mask boundaries.
This thesis empirically shows that DET can successfully improve segmentation boundaries, but the practical results suggest that further refinement is needed. Additionally, the results show improvements when using the new compounding loss ABL(RMI) compared to its predecessor, ABL(CE) which substitutes RMI with Cross-Entropy loss(CE). | |
