# Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN
## 5 Experiments and Results
### Comparative Experiment  
As a baseline to validate the effectiveness of our method, we perform an experiment on our dataset, leaving the settings of the modified Faster R-CNN on the default parameters in which the pre-trained model is ResNet-50 networks except that the total stride on the last shared convolutional layer is 16 pixels in contrast. In this case, we only use the feature maps output by the last residual block of second two stages which are denoted as res4f and res5c in the Figure 2 respectively. Similarly, we up-sample the feature maps of the last layer of higher stages by a factor of 2 using the bilinear  
Figure 5: Some samples from SORSI dataset. Figure 6: Histogram of object sizes class by class. interpolation method for simplicity. Before that, we adopt a 1 \(\times\) 1 convolutional layer to reduce channel dimensions. After merging the two feature outputs, a 3 \(\times\) 3 convolutional layer is appended to generate the final feature map. Furthermore, a few experiments are separately performed to evaluate the impact of using appropriate anchor boxes by setting different anchor scales. We report the results with using various strategies during training the modified Faster R-CNN on SORSI dataset in Table 1. The performance of the baseline is 66.6% mAP where the AP is 58.2% for ship and 75.0% for plane. Apparently, we find that the modified Faster R-CNN with the total stride of 8 achieves better performance than the baseline model with the total stride of 16 by a large margin, especially for the ship whose areas of bounding boxes are between \(10^{2}\) and \(50^{2}\) pixels. Besides, it is evidently shown that using appropriate anchor boxes can conduce to boosting the detection performance by almost two percentage points. This may bring in some insights about how to choose appropriate anchor boxes according to the existing dataset. Based on this conclusion, all of the follow-up experiments will adopt three scales with box areas of \(16^{2}\), \(40^{2}\), and \(100^{2}\) pixels. Due to the non-uniform class distribution, the plane class cannot be trained enough because the batch size has to be fixed to 1 for RPN stage. The relevant experimental results indicate that the balanced sampling strategy contributes to increasing the plane AP by 7.6% while the ship AP only decreases by 1.2%, which proves that the BS strategy is able to solve the effect of the non-uniform class distribution in some degree. Furthermore, we conduct several experiments to investigate the behavior of the proposed RR as a data augmentation technique. In the experiments, the probability \(p\) is set at 0.5 while the rotate angle \(\theta\) is set at \(10^{\circ}\). We observe that the behavior of the proposed RR is somewhat similar to that of BS which enlarges our training set equivalently. When using the contextual detection model during training, it is noted that we only obtain low-level improvements by 0.7% and 0.5%, respectively. Besides, when adopting any two kinds of the three strategies during training, we find that it can contribute to achieve better performance combining the balanced sampling strategy and the contextual detection model. At last, the best performance can be obtained by combining the three aforementioned strategies. By comparing and analyzing the multiple groups of experiments, the validity of the proposed structure is verified. Our modified Faster R-CNN delivers very impressive performance on detecting small objects in optical remote sensing images. However, it can be seen from Table 1 that the mAP still has room for improvement. Through observation of the test results, we attribute this to two points: false alarm and misjudgment. Some test results are shown in Figure 7 on the test set of our SORSI dataset.