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Litao Han

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Proceedings Article | 1 June 2023 Paper

Multitask floor plan analysis based on multi-scale and self-attention

Litao Han, Hu Qiao, Huixia Guo, Mengfan Liu

Proceedings Volume 12710, 1271005 (2023) https://doi.org/10.1117/12.2682661

KEYWORDS: Buildings, Semantics, Image segmentation, Convolution, Data modeling, Neural networks, Modeling

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With the continuous acceleration of urbanization and the rapid development of modern architectural technology, there are increasingly large public buildings and more complex indoor space patterns. At present, there are two main challenges in the analysis of interior floor plans: the first is that the objects in the image show scale diversity; the second is that due to the limitations of the convolution layer itself, ordinary convolutional neural networks have some difficulties in capturing global semantic information. In this paper, we propose a multi-task convolutional neural network model based on the multi-scale and polarized self-attention mechanism to improve the multi-scale information aggregation and global semantic information capture capabilities of semantic segmentation networks. Experiments on the public data set CubiCasa5K show that the proposed model can more accurately complete the identification of building components and the extraction of spatial area information in the floor plan.

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