# Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach
## 6 Conclusions and Future Work  
We introduce an end-to-end trainable network for detecting furniture objects in floor plan images. Our proposed method incorporates the high-level architectural principle of traditional object detection approaches. Specifically, we exploit and compare traditional convolution and deformable convolution approaches to detect furniture objects in the floor plan images using Cascade Mask R-CNN [15]. Our different experiments and modifications will help to achieve better generalization and detection performance. We achieve state-of-the-art performance on COCO primary challenge matrices (AP at IoU = 0.50:0.05:0.95) with the mAP score of 0.998 on our SFPI dataset. With our proposed method, we achieved a mAP score of 0.982 on the publicly available SESYD [3] dataset. Our literature survey identified no significant public dataset available for floor plan images that can be used to train deep learning detectors. We try filling this gap by creating a custom dataset SFPI containing 10,000 floor plan images with 316,160 object instances available in these images. There are 16 different furniture classes and ten different floor plans. This dataset can be further extended using our scripts and can quickly adapt to new furniture classes. Moreover, the presented work empirically establishes that it is possible to achieve state-of-the-art object detection in floor plan images.  
For future work, we expect our SFPI dataset to be embedded with more floor plan layouts and different furniture objects to make a more generalized floor plan dataset.  
Figure 14: Class-wise average precision comparison Conventional Convolutional Network (CNN) and Deformable convolutional Network (DCN) [16]. The results have been taken from experiments Our_SFPI_SEYD_train_SEYD_test_CNN and Our_SFPI_SEYD_train_SEYD_test_DCN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **AP** & **Category** & **AP** & **Category** & **AP** \\ \hline Armchair & 0.997 & Bed & 1.000 & Door1 & 1.000 \\ \hline Door2 & 1.000 & Sink1 & 0.996 & Sink2 & 0.997 \\ \hline Sink3 & 0.999 & Sink4 & 1.000 & Sofa1 & 0.999 \\ \hline Sofa2 & 0.999 & Table1 & 1.000 & Table2 & 0.998 \\ \hline Table3 & 1.000 & Tub & 1.000 & Window1 & 0.994 \\ \hline Window2 & 0.995 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 8: Class-wise average precision (AP) from the results of our experiment Our_SFPI _SEYD_train_SEYD_test_DCN.  
A deeper backbone would be able to improve the performance without using deformable convolution. Moreover, these experiments can be used in different floor plan applications such as interactive image-fitting and floor plan text generation, helping visually impaired people with floor plans. Earlier, all these applications used Faster R-CNN [12], but now with our experiments, it is evident that Cascade Mask R-CNN [15] performs better in these applications.  
Conceptualization, S.M. and K.A.H.; writing--original draft preparation, S.M. and K.A.H.; writing--review and editing, S.M., K.A.H. and M.Z.A.; supervision and project administration, M.L., A.P. and D.S. All authors have read and agreed to the submitted version of the manuscript.  
The work leading to this publication has been partially funded by the European project INFINITY under Grant Agreement ID 883293.  
Not applicable.  
The authors declare no conflict of interest.