# Embedded Deep Learning for Ship Detection and Recognition
## 5 Experiment Results
### System Performance  
ESDR-DL is deployed to Dongying port, China. The video cameras used are Hikvision DS-2 CD3T25D-I5. The pixel used is 1920 \(\times\) 1080 and the frame rate is 30 fps. We use seven TX2s for 10 cameras as shown in Table 7. Four cameras are installed on both sides of the entrance with a height of 8 m. Others are installed inside the port.  
During one month's running, we collect 13,000 recognized records and checks the accuracy manually. There are a total of 14,536 ships in videos.  
Table 8 illustrates the recall and precision rates of ship detection and recognition. S denotes the ship number occurring in each camera, D-P stands for the ship detection precision, D-R is the detection recall, R-P is the recognition precision, R-R refers to the recognition recall, and T denotes the processing efficiency of each camera.  
Comparing Tables 6 and 8, we can find that the accuracy of actual running is lower than the accuracy testing in the home-made data set because there are new ships arriving the port and the ESDR-DL can not recognize these new ships. In addition, ESDR-DL performs better for the inside-port monitoring cameras because there are some far away scenes of ships from the entrance cameras and only close scenes exist from the inside-port cameras, while DCNet focus on big target detection and recognition. In addition, as shown in Figure 7, the system can run in bad weather conditions (such as rain and smog) in practice. In order to test the performance of the system in bad weather, we run the system in rain and smog weather, and run it at dusk (5:00 p.m.-6:00 p.m.). The detection recognition results are shown in Table 9.  
The recognition results are shown in Table 10. We can see that the accuracy of the system is dropping sharply in rain and smoggy weather, while performing well at dusk. This is not a problem in practice as there are very few ships in such weather conditions.  
\begin{table}
\begin{tabular}{l l} \hline TX2-1 & entrance camera-1 \\ TX2-2 & entrance camera-2 \\ TX2-3 & entrance camera-3 \\ TX2-3 & entrance camera-4 \\ TX2-4 & inside port camera-1 \\ TX2-5 & inside port camera-2 \\ TX2-6 & inside port camera-3 \\ TX2-6 & inside port camera-4 \\ TX2-7 & inside port camera-5 \\ TX2-7 & inside port camera-6 \\ \hline \end{tabular}
\end{table}
Table 7: Deployment of cameras and TX2s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Camera** & **S** & **D-P** & **D-R** & **R-P** & **R-R** & **T** \\ \hline entrance camera-1 & 903 & 0.86 & 0.80 & 0.82 & 0.74 & 27 fps \\ entrance camera-2 & 903 & 0.86 & 0.79 & 0.82 & 0.74 & 27 fps \\ entrance camera-3 & 887 & 0.85 & 0.79 & 0.81 & 0.74 & 27 fps \\ entrance camera-4 & 891 & 0.86 & 0.80 & 0.80 & 0.73 & 27 fps \\ inside port camera-1 & 1532 & 0.89 & 0.84 & 0.84 & 0.79 & 13 fps \\ inside port camera-2 & 1129 & 0.87 & 0.82 & 0.80 & 0.75 & 13 fps \\ inside port camera-3 & 1413 & 0.90 & 0.85 & 0.85 & 0.78 & 13 fps \\ inside port camera-4 & 1410 & 0.89 & 0.84 & 0.84 & 0.79 & 13 fps \\ inside port camera-5 & 1611 & 0.89 & 0.85 & 0.82 & 0.79 & 13 fps \\ inside port camera-6 & 1611 & 0.88 & 0.85 & 0.82 & 0.80 & 13 fps \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of ESDR-DL.