Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Towards Robust Object detection in Floor Plan Images: A Data Augmentation Approach

Version 1 : Received: 5 October 2021 / Approved: 5 October 2021 / Online: 5 October 2021 (15:09:26 CEST)

A peer-reviewed article of this Preprint also exists.

Mishra, S.; Hashmi, K.A.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, M.Z. Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach. Appl. Sci. 2021, 11, 11174. Mishra, S.; Hashmi, K.A.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, M.Z. Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach. Appl. Sci. 2021, 11, 11174.

Journal reference: Appl. Sci. 2021, 11, 11174
DOI: 10.3390/app112311174

Abstract

Object detection is one of the most critical tasks in the field of Computer vision. This task comprises identifying and localizing an object in the image. Architectural floor plans represent the layout of buildings and apartments. The floor plans consist of walls, windows, stairs, and other furniture objects. While recognizing floor plan objects is straightforward for humans, automatically processing floor plans and recognizing objects is a challenging problem. In this work, we investigate the performance of the recently introduced Cascade Mask R-CNN network to solve object detection in floor plan images. Furthermore, we experimentally establish that deformable convolution works better than conventional convolutions in the proposed framework. Identifying objects in floor plan images is also challenging due to the variety of floor plans and different objects. We faced a problem in training our network because of the lack of publicly available datasets. Currently, available public datasets do not have enough images to train deep neural networks efficiently. We introduce SFPI, a novel synthetic floor plan dataset consisting of 10000 images to address this issue. Our proposed method conveniently surpasses the previous state-of-the-art results on the SESYD dataset and sets impressive baseline results on the proposed SFPI dataset. The dataset can be downloaded from SFPI Dataset Link. We believe that the novel dataset enables the researcher to enhance the research in this domain further.

Keywords

Object Detection; Cascade Mask R-CNN; Floor Plan Images; Deep Learning; Transfer Learning; Dataset Augmentation; Computer Vision

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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