Enhanced Object Detection in Floor-plan through Super Resolution

TL;DR

This paper introduces a stacked model combining Super-Resolution and object detection techniques to improve floor plan image analysis, achieving nearly 40% better detection accuracy.

Contribution

It presents a novel multi-component CNN model that stacks Super-Resolution with object detection for enhanced floor plan analysis.

Findings

Super-Resolution improves detection accuracy by 39.47%.

Stacked model outperforms vanilla object detection.

Public dataset and code are available.

Abstract

Building Information Modelling (BIM) software use scalable vector formats to enable flexible designing of floor plans in the industry. Floor plans in the architectural domain can come from many sources that may or may not be in scalable vector format. The conversion of floor plan images to fully annotated vector images is a process that can now be realized by computer vision. Novel datasets in this field have been used to train Convolutional Neural Network (CNN) architectures for object detection. Image enhancement through Super-Resolution (SR) is also an established CNN based network in computer vision that is used for converting low resolution images to high resolution ones. This work focuses on creating a multi-component module that stacks a SR model on a floor plan object detection model. The proposed stacked model shows greater performance than the corresponding vanilla object detection model. For the best case, the the inclusion of SR showed an improvement of 39.47% in object detection over the vanilla network. Data and code are made publicly available at https://github.com/rbg-research/Floor-Plan-Detection.