Floor plan recognition and vectorization using combination unet, faster-rcnn, statistical component analysis and ramer-douglas-peucker

Abstract

The floor plan recognition and vectorization problem from the image has a high market response due to the ability to be applied in such domains as design, automatic furniture fitting, property cost estimation, etc. Several approaches already exist on the market. Many of them are using just statistical or deep machine learning methods capable of recognizing a limited set of floor plan types or providing a semi-automatic tool for recognition. This paper introduces the approach based on the combination of statistical image processing methods in a row of machine learning techniques that allow training robust model for the different floor plan topologies. Faster R-CNN for the floor object detection with a mean average precision of 86% and UNet for the wall segmentation has shown the IoU metric results of about 99%. Both methods, combined with functional and component filtration, made it possible to implement the new approach for vectoring the floor plans.