# Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach
## 5 Experimental Results
### Results and Discussion
#### 5.3.2 SFPI Dataset  
We perform multiple experiments with the SFPI dataset by dividing the dataset in different ways. Before we dive deeper into different experiments and their details, first, we lay down some experiment labels for better understanding in Table 3. These labels will be used throughout the paper. In general, the used naming convention is model_dataset_train_dataset_test. In our SFPI dataset, we have 10,000 images to perform experiments. First, we will present the results between the SESYD [3] dataset and our SFPI dataset in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Mean Average Precision**} & \multicolumn{2}{c}{**Mean Average Recall**} \\  & & \multicolumn{2}{c}{**(mAP)**} & \multicolumn{2}{c}{**(mAR)**} \\ \hline
**Method** & **Object** & **Val** & **Test** & **Val** & **Test** \\ \hline Our\_SESYD\_train\_test & 20,670 & 0.981 & 0.982 & 0.986 & 0.987 \\ \hline Our\_SFPI\_train\_test & 316,160 & 0.995 & 0.995 & 0.997 & 0.997 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative analysis of our proposed model on SESYD [3] dataset and SFPI dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Mean Average Precision (mAP)**} & \multicolumn{2}{c}{**Mean Average Recall (mAR)**} \\ \hline
**Model\_Dataset** & **Objects** & **Val** & **Test** & **Val** & **Test** \\ \hline Our\_SESYD & 20,670 & 0.981 & 0.982 & 0.986 & 0.987 \\ \hline [PERSON] et al. [9]—d1 & 1111 & - & 0.31 & - & 0.60 \\ \hline [PERSON] et al. [9]—d2 & 1111 & - & 0.39 & - & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative analysis of our model with existing state-of-the-art methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Experiment Label** & **Train** & **Val** & **Test** \\ \hline Experiment-1 & Our\_SFPI\_train\_test & SFPI & SFPI & SFPI \\ \hline Experiment-2 & Our\_SFPI\_train\_SESYD\_test & SFPI & SESYD [3] & SESYD [3] \\ \hline Experiment-3 & Our\_SFPI\_SESYD\_train\_SESYD\_test & SFPI + SESYD [3] & SESYD [3] & SESYD [3] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Explanation of different experiments and dataset associated with it. For our SFPI dataset, we followed the 70-15-15 rule to split the dataset. We take 7000 images for training and 1500 images for validation and testing. With the number of increased images and objects, we can see the improvement in the results of our proposed model. We achieve a 0.995 mAP score and 0.997 mAR score. This clearly shows that our model performs better on the SFPI dataset where we have sufficient images to train a model as compared to less number of images we have in SESYD [3]. We further execute more experiments, including the SFPI and SESYD [3] datasets, to get more generalized results from our end-to-end model. In the second experiment Our_SFPI_train_SESYD_test mentioned in Table 5, we use the full SFPI dataset for training, which means all 10,000 images are used to train our end-to-end model. We use the SESYD [3] dataset for validation and testing. We perform a random split on the SESYD [3] dataset and use 500 images for validation and 500 for testing. In this way, we can compare how our network performs with a generalized dataset. Moreover, we can establish similarities and dissimilarities between our SFPI dataset and the SESYD [3] dataset. We can achieve good results in this experiment if we compare it to the results of [PERSON] et al. [9]. Figure 9 is the output of the experiment Our_SFPI_train_SESYD_test. Few classes are misclassified; for the most part, the network confuses between armchair, sofa, and bed classes. We see many instances where sofa or armchair classes are recognized as the bed. This might be because of the data augmentation we put in the SFPI dataset, whereas in SESYD [3] furniture objects are not that much augmented, and in some scenarios sofa and armchair resembles a bed. To improve the results, we perform our next experiment, Our_SFPI_SESYD_train_SESYD_test, where we use close domain fine-tuning.