# Probabilistic Ship Detection and Classification Using Deep Learning
## 5 Experimental Results
### Performance
#### 5.2.2 Results of Detection Based on Video  
Seven sequences of images were used to demonstrate the performance of the proposed method based on video. Among them, two videos were considered in detail. In the first sequence involving a tugboat, the weather was relatively fine and the ships were not influenced by environmental factors. In the second sequence involving a destroyer, however, the weather was windy and the environmental factors, such as waves and wind, influence ships. The Faster R-CNN returned eight confidences, one for each class, and the eight confidences equaled 1 in each frame, as shown in Equation (17). In Figure 7, the changes in the eight confidences are plotted against the frames for the first sequence. The subfigures in the first, second and third rows correspond to the Faster R-CNN; Faster R-CNN and IoU tracking; and Faster R-CNN, IoU tracking and Bayesian fusion, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **AP (\%)** \\ \hline Aircraft carrier & 90.56 \\ Destroyer & 87.98 \\ Submarine & 90.22 \\ Container ship & 99.60 \\ Bulk carrier & 99.59 \\ Cruise ship & 99.59 \\ Tugboat & 95.01 \\ \hline mAP & 94.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the single image detection. Figure 6: Results of the ship detection using the Faster R-CNN based on the test images. The confidence in Figure 7a implies \(p_{k}^{t}=p(\omega=k|B^{t})\) and the confidence in Figure 7c implies \(f_{k}^{t}=p(\omega=k|B^{1:t})\). In the first sequence, the target ship is a tugboat. In Figure 7a, the tugboat is classified as a bulk carrier four times by Faster R-CNN. The confidence of the tugboat also does not remain steady but changes irregularly. In Figure 7b, IoU tracking is also used with the Faster R-CNN. When the target was not detected or the IoU was lower than the threshold, the bounding box was considered background and the corresponding confidences were assigned by Equation (18). However, in the figure, no background confidence was observed since all the targets in each frame were detected by the Faster R-CNN and the IoU values from IoU tracking were higher than the threshold. Figure 7c shows the experimental result when Faster R-CNN, IoU tracking and Bayesian fusion were used together. The confidence for the tugboat was steady and approached one after a few frames, and the confidences for the other classes disappeared and approached zero. The experimental results for the second sequence are shown in Figure 8. Unlike the first sequence, the ships were affected by environmental factors. The target ship in the second sequence was a destroyer. Similar to Figure 7, the change in the eight confidences is plotted against frames for the second sequence in Figure 8. The subfigures in the first, second and third rows in Figure 8 correspond to Faster R-CNN; Faster R-CNN and IoU tracking; and Faster R-CNN, IoU tracking and Bayesian fusion, respectively. Figure 7: The change of confidences in the image sequence without environmental factors. (**a**) Faster R-CNN, (**b**) Faster R-CNN and IoU tracking, and (**c**) Faster R-CNN, IoU tracking, and Bayesian fusion. First, let us consider Intervals 1 and 4 in Figure 8. In the two intervals, the destroyer was falsely classified as an aircraft carrier, as shown in Figure 8a,b. In particular, the target ship was classified not as a destroyer but as an aircraft carrier in six frames in a row from frame 235 to 240. However, the proposed method overcame the false classification problem and the confidence for the destroyer progressed steadily to one, as shown in Figure 8c. Second, consider Interval 2. In this interval, the destroyer was not detected and was actually classified as background by the IoU tracking several times. However, the proposed method again overcame the misdetections again and the confidence for a destroyer progressed steadily to one, as shown in Figure 8c. Third, consider Interval 3, which was slightly different from Intervals 1, 2 and 4.