# Mask-Aware Semi-Supervised Object Detection in Floor Plans
## 8 Conclusions and Future Work  
We examine the capabilities of the semi-supervised approach to detect objects in floor-plan data. It pulls information from the teacher network and feeds it to the student network. The teacher model uses label data to form pseudo-boxes, and the student model uses both label data (with the pseudo boxes) and labeled data as ground truth for training. On Mask R-CNN [17] detector with ResNet-101 backbone, the proposed approach achieves 98.8(%) mAP, 99.7(%) mAP, 99.8(%) mAP with 1%, 5%, and 10% labeled data, respectively. We can observe from the results that we can obtain the best performance by just using 1% labeled data. Furthermore, this experiment can be implemented in various floor-plan applications \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Detector** & 1\% & **5\%** & **10\%** \\ \hline
**Supervised** & **Mask R-CNN** & **92.26 \(\pm\) 0.16** & **92.89 \(\pm\) 0.15** & **93.16 \(\pm\) 0.12** \\ \hline STAC [13] & Faster R-CNN & 94.86 \(\pm\) 0.12 (+2.6) & 95.43 \(\pm\) 0.14 (+2.54) & 97.12 \(\pm\) 0.15 (+3.96) \\ \hline Unbiased Teacher [66] & Faster R-CNN & 96.12 \(\pm\) 0.143 (+3.86) & 96.87 \(\pm\) 0.15 (+3.98) & 97.18 \(\pm\) 0.12 (+4.02) \\ \hline Label Match [67] & Faster R-CNN & 98.1 \(\pm\) 0.12 (+6.01) & 98.54 \(\pm\) 0.16 (+5.65) & 99.1 \(\pm\) 0.12 (+5.94) \\ \hline
**Mask-Aware (Our)** & **Faster R-CNN** & **98.27 \(\pm\) 0.20 (+6.01)** & **99.74 \(\pm\) 0.25 (+6.85)** & **99.5 \(\pm\) 0.15 (+6.43)** \\ \hline
**Mask-Aware (Our)** & **Mask R-CNN** & **98.8 \(\pm\) 0.10 (+6.54)** & **99.7 \(\pm\) 0.15 (+6.81)** & **99.8 \(\pm\) 0.10 (+6.64)** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Previously comared semi-supervised detectors with our approach trained on an average of 5 data folds with 1%, 5%, and 10% SFPI label dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Approach\_Dataset** & **Detector** & **mAP** \\ \hline [PERSON] et al. [68] & supervised\_d1 & Faster R-CNN & 0.31 \\ \hline [PERSON] et al. [68] & supervised\_d2 & Faster R-CNN & 0.39 \\ \hline [PERSON] et al. [69] & supervised\_(\_\)(SEYD+ROBIN) & Faster R-CNN & 0.756 \\ \hline [PERSON] et al. [69] & supervised\_(\_\)([PERSON]+ROBIN) & YOLO & 0.857 \\ \hline [PERSON] et al. [20] & supervised\_SFPI & Cascade Mask R-CNN & 0.995 \\ \hline Ours & semi-supervised\_SFPI & Faster R-CNN & 0.996 \\ \hline Ours & semi-supervised\_SFPI & Mask R-CNN & 0.998 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Previously compared supervised detectors with our semi-supervised approach that is trained on the floor-plan dataset using Mask R-CNN [17] and Faster R-CNN [25] with ResNet-101 [19] backbone on 10% label data. *We cannot directly compare the results of [PERSON] et al. [68] because of the different datasets. such as floor-plan text generation, and furniture fitting, helping impaired people to analyze house design and for customers to buy a house online. Earlier, all these applications used supervised learning approaches [68, 69] for floor-plan object detection. However, now with our experiment, it is clear that the semi-supervised [16] approach gives better results for these applications. In future, we can improve Mask R-CNN [17]-based semi-supervised floor-plan detection system in different ways. We can add text information to detect room types, especially rooms that are not physically separated, like the dining hall attached to the kitchen. We can also label rooms according to their functionality. Further research using noisy labels in training and uncertainty estimation are also a few important topics to boost the efficiency of semi-supervised-based object detection. Writing--original draft preparation, T.S., K.A.H., M.Z.A. ; writing--review and editing, T.S., K.A.H., M.Z.A. ; supervision and project administration, A.P., D.S. and M.L. 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.