# Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach
## 5 Experimental Results
### Results and Discussion
#### 5.3.2 SFPI Dataset
In Close domain fine-tuning, we fine-tune models using datasets that are closer to the domain of our problem rather than using natural images, which we do when we apply fine-tuning. In our next experiment Our_SFPI_SESYD_train_SESYD_test, we combine both the SFPI dataset and the SESYD [3] dataset. For training, we use 10,500 images, out of which 10,000 images are from the SFPI dataset, and we pick 500 random images from SESYD [3] dataset. Out of the remaining 500 images of the SESYD [3] dataset, 250 are used for validation, and 250 are used for testing. With the close domain fine-tuning, our model improves, and we get better results. We can achieve a 0.997 mAP score and 0.998 mAR score, which is even better than our experiment Our_SESYD_train_test, where we used the SFPI dataset only for training, validation, and testing. This indicates the advantages of using closed domain fine-tuning. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Mean Average**} & \multicolumn{3}{c}{**Mean Average**} \\  & \multicolumn{3}{c}{**Precision (mAP)**} & \multicolumn{3}{c}{**Recall (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 Our\_SFPI\_train\_SESYD\_test & 336,830 & 0.751 & 0.750 & 0.775 & 0.775 \\ \hline Our\_SFPI\_SESYD\_train\_SESYD\_test & 336,830 & 0.997 & 0.997 & 0.998 & 0.998 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative analysis of different experiments performed on our proposed model. Figure 10 is the final output of our proposed model in the case of experiment Our_SFPI_SESYD_train_SESYD_test. The image shows that all furniture objects are correctly classified with a good confidence score. In image Figure 10 we can observe good furniture augmentation, as discussed earlier. Our proposed model can generalize well given the context of the two datasets SFPI and SESYD [3] object detection and localization worked perfectly. Figure 9: Qualitative results of our proposed model from experiment Our_SFPI_train_SESYD_test. Many miss-classified classes are visible in the image, such as Bed and Sofa. In Table 6, we described the class-wise average precision score achieved in our experiment Our_SFPL_SESYD_train_SESYD_test. It is visible from the Table 6 that for few classes, we have reached the average precision of one like Door, Bed, and Tub, whereas for the other remaining classes, the score is high, and except Window1 class, all other classes already reached above 0.90 average precision. This class-wise AP result gives us more clarity about model performance. We can identify where our model works well and what classes are causing problems. For completeness of the paper, we computed the mAP score on various IoU thresholds ranging from 0.5 to 1.0. We performed this for all of our three experiments. Figure 11 illustrates the performance of our approach in terms of mean average precision. We can see that we can achieve mAP score of one for Our_SESYD_train_test, 0.861 in the case of Our_SFPI_train_SESYD_test, and 0.936 for Our_SFPI_SESYD_train_SESYD_test when the IoU is set to 0.5. From this point onwards, as we increase, the IoU mAP is decreasing for the latter mentioned two experiments. For experiment Our_SFPI_train_SESYD_test and experiment Our_SFPI_SESYD_train_SESYD_after the IoU threshold of 0.8, mAP is equal. The mAP score eventually reaches zero when we set the IoU to 1 for all experiments. Figure 10: Sample output from experiment Our_SFPI_SESYD_train_SESYD_test. It is evident that all furniture objects are identified and localized correctly. Until this point, we were only deploying conventional convolutional networks, but we want to apply our model with deformable convolution network (DCN) [16] as well. DCN [16] could be useful for our datasets as they can easily adapt the shape of unknown complex transformations in the input.