Abstract

In automated document analysis, floorplans are a concern for several years and algorithmic approaches have been used until recently. This problem has also improved output with the emergence of Convolutionary neural networks (CNN). In this study, it is the task to retrieve space and geometric data from planes as accurately as possible and the bulk of the information is extracted from a plane image by means of instance segments, such as the Cascade Mask R-CNN. A new way of using keypoint CNN is presented in order to supplement the segmentation, so that precision corner positions can be identified. Then they are coupled to the resulting segmentation in a post-processing stage. With an average IoU of 72.7% compared to 57.5%, the resulting segmentation scores surpass the existing benchmark for CubiCasa5k floorplan datasets. In addition, the mean IoU is increased in almost every class for individual classes. Cascade masks R-CNN have also shown to be more appropriate for this mission than R-CNN masks.

Details

Title
Segmentation of Spatial and Geometric Information from Floorplans using CNN Model
Author
Ramasamy, Anusuya 1 ; Rajan, M Sundar 2 ; Arunkumar, J R 3 

 Assistant Professor, Faculty of Computing and Software Engineering, Arbaminch Institute of Technology, Arbaminch University, Ethiopia. [email protected] 
 Associate Professor, Faculty of Electrical and Computer Engineering, Arbaminch Institute of Technology, Arbaminch University, Ethiopia. [email protected] 
 Associate Professor, Faculty of Computing and Software Engineering, Arbaminch Institute of Technology, Arbaminch University, Ethiopia. [email protected] 
Pages
1909-1920
Section
Research Article
Publication year
2021
Publication date
2021
Publisher
Ninety Nine Publication
e-ISSN
13094653
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2623459314
Copyright
© 2021. This work is published under https://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.