A faster r-cnn approach for extracting indoor navigation graph from building designs

Abstract

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.