Semantic and Instance Segmentation of Room Features in Floor Plans using Mask R-CNN
Abstract: Machine learning techniques within Computer Vision are rapidly improving computers' high-level understanding of images, thus revealing new opportunities to accomplish tasks that previously required manual intervention from humans. This paper aims to study where the current machine learning state-of-the-art is when it comes to parsing and segmenting bitmap images of floor plans. To assess the above, this paper evaluates one of the state-of-the-art models within instance segmentation, Mask R-CNN, on a size-limited and challenging floor plan dataset. The model handles detecting both objects and generating a high-quality segmentation map for each object, allowing for complete image segmentation using only a single network. Additionally, in order to extend the dataset, synthetic data generation was explored, and results indicate that it aids the network with floor plan generalization. The network is evaluated on both semantic and instance segmentation metrics and results show that the network yields good, almost completely segmented floor plans on smaller blueprints with little noise, while yielding decent but not completely segmented floor plans on large blueprints with a large amount of noise. Based on the results and them being achieved from a limited dataset, Mask R-CNN shows that it has potential in both accuracy and robustness for floor plans segmentation, either as a standalone network or alternatively as part of a pipeline of several methods and techniques.
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