A FASTER R-CNN APPROACH FOR EXTRACTING INDOOR NAVIGATION GRAPH FROM BUILDING DESIGNS

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Song, Y. Q.^2¹  Niu, L.^1² 

[1] School of Geographic and Environmental Science, Normal University of Tianjin, West Bin Shui Avenue, 300387 Tianjin, China^2;School of Surveying and Urban Spatial Information, Henan University of Urban Construction, 467036 Pingdingshan, China^1

关键词: faster R-CNN;    indoor;    extraction;    navigation graph;    building design;   

DOI  :  10.5194/isprs-archives-XLII-2-W13-865-2019

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

The indoor navigation graph is crucial for emergency evacuation and route guidance. However, most of existing solutions are limited to the tedious manual solutions and inefficient automatic solutions of the indoor building designs. In this paper, we strive to combine the cutting-edge faster R-CNN deep learning models with spatial connection rules to provide fine quality indoor navigation graphs. The extraction experiment result is convincing for general navigation purpose. But there exist several shortages for faster R-CNN models to overcome, such as optimizations of the complex object detections and ability of handling irregular shape regions for indoor navigation graph extractions.

【 授权许可】

CC BY