# Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN
## 4 Experiments and Results
### Proposed Improved Faster R-CNN Compared with Other Studies  
In order to evaluate the performance of our proposed model on the VHR10 dataset objectively, we compared it with other models using the same dataset in the field. Comparison results are shown in Table 2. Values marked in bold are the highest AP values obtained in the class.  
When Table 2 is examined, it is obvious that the method we propose gives better results compared to other studies. We achieved a 1.7% \(m\)AP increase over DODN, which shows by far the best performance in other studies. This is because firstly, DCN has feature extraction from more receptive areas compared to CNN, and secondly, FPN's features obtained from different layers are given to the detection network. In addition, the use of WS instead of BN resulted in successful training with the Nvidia GTX 1080 GPU, which is a very weak GPU when compared to very powerful GPUs such as Nvidia TITAN X or Nvidia TITAN XP.  
Although our method provides the best performance, detection accuracy for the bridge object category is still low. The reason for this is the imbalance between classes in the dataset we used in the study. This affected the results. While the object detection success of the classes with a higher number of training samples (e.g., plane) increased, the success rate of the classes with fewer training samples (e.g., bridge) decreased. Data augmentation did not change this result. This problem can be solved using the focal loss, loss function [22] which is proposed to eliminate imbalance between classes. In future studies, the model we propose aims to eliminate the imbalance between classes by using focal loss.  
Using our improved faster R-CNN model and class-specific object category classifiers, we performed ten classes of object detection in our test data set. Figure 9 shows a series of object detection results of the proposed model in which true positives, false positives, and false negatives are represented respectively by green, red, and blue rectangles. Despite the major changes in the orientation and size of the objects, the proposed model successfully identified and localized most of the objects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **COPD [9]** & **RICNN [11]** & **RI-CAO [12]** & **DODN [13]** & **Ours** \\ \hline
**Plane** & 0.6225 & 0.8835 & 0.9970 & 0.9392 & 0.9958 \\
**Ship** & 0.6937 & 0.7734 & 0.9080 & 0.9297 & 0.8813 \\
**Storage Tank** & 0.6452 & 0.8527 & 0.9061 & 0.9925 & 0.9378 \\
**Baseball Diamond** & 0.8213 & 0.8812 & 0.9291 & 0.9633 & 0.9788 \\
**Tennis Court** & 0.3413 & 0.4083 & 0.9029 & 0.9612 & 0.8576 \\
**Basketball Court** & 0.3525 & 0.5845 & 0.8013 & 0.7097 & 0.9306 \\
**Ground Track Field** & 0.8421 & 0.8673 & 0.9081 & 1.0 & 0.9767 \\
**Harbor** & 0.5631 & 0.6860 & 0.8029 & 0.9688 & 0.9744 \\
**Bridge** & 0.1643 & 0.6151 & 0.6853 & 0.8115 & 0.6897 \\
**Vehicle** & 0.4428 & 0.7110 & 0.8714 & 0.8009 & 0.9352 \\
**\(m\)AP** & 0.5489 & 0.7263 & 0.8712 & 0.9077 & 0.9231 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of different models on the NWPU-VHR10 [8] dataset.