## 6 Experimental Results
### Detection Speed in FPS  
In Section 6.3, we used the amount of parameters and the amount of computations to represent the computation efficiency. In this section, we adopted frames per second (FPS) to show the detection speed. Remember that the whole process of ship detection not only includes a stage processed by a neural network but also a post-processing stage used to decide a unique rectangular box. As FPS is decided by both the stages, we separated them in the evaluation results. We used an Nvidia Tesla V100 to conduct the testing on HRSID and SSDD and set \(batchsize=128\). Results are provided in Tables 7 and 8. In the two given tables, there are five columns referring to FPS. Let us first focus on the first three columns referring to FPS, where \"Net\" represents the processing speed of the neural network stage, \"Post\" represents the speed of the post-processing stage, and \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Architectures** & **Channels** & **Paras** & **FLOPs** & **AP** & **F1** \\ \hline S\_Arch\_32 & \(32\times 4\) & 274 K & 5.96 G & 91.35 & 88.46 \\ S\_Arch\_16 & \(16\times 4\) & 84 K & 1.90 G & 93.65 & 91.57 \\ S\_Arch\_8 & \(8\times 4\) & 30 K & 0.59 G & 92.28 & 90.29 \\ S\_Arch\_4 & \(4\times 4\) & 12 K & 0.22 G & 90.99 & 88.68 \\ \hline S\_Arch\_C\_32 & \(32\times 4\) & 251 K & 5.99 G & 91.68 & 89.65 \\ S\_Arch\_C\_16 & \(16\times 4\) & 59 K & 1.62 G & 87.41 & 88.98 \\ S\_Arch\_C\_8 & \(8\times 4\) & 24 K & 0.54 G & 89.61 & 90.24 \\ S\_Arch\_C\_4 & \(4\times 4\) & 9 K & 0.21 G & 89.14 & 88.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detection results over SSDD (the first four are obtained by setting \(ops\_ratio=0\), and the last four are obtained by setting \(ops\_ratio=0.003\)). \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Architectures** & **Channels** & **FLOPs** & **Net** & **Post** & **All** & \(Post_{mt}\) & **All\({}_{mt}\)** \\ \hline H\_Arch\_32 & \(32\times 4\) & 6.89 G & 279 & 492 & 178 & 3955 & 261 \\ H\_Arch\_16 & \(16\times 4\) & 1.84 G & 561 & 488 & 261 & 3923 & 491 \\ H\_Arch\_8 & \(8\times 4\) & 0.62 G & 1028 & 481 & 327 & 3859 & 812 \\ H\_Arch\_4 & \(4\times 4\) & 0.21 G & 1855 & 475 & 379 & 3764 & 1243 \\ \hline H\_Arch\_C\_32 & \(32\times 4\) & 5.64 G & 256 & 563 & 176 & 4525 & 242 \\ H\_Arch\_C\_16 & \(16\times 4\) & 1.64 G & 496 & 551 & 261 & 4430 & 446 \\ H\_Arch\_C\_8 & \(8\times 4\) & 0.58 G & 936 & 560 & 312 & 4501 & 775 \\ H\_Arch\_C\_4 & \(4\times 4\) & 0.18 G & 1828 & 555 & 377 & 4452 & 1297 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Computation efficiency in FPS (HRSID). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Architectures** & **Channels** & **FLOPs** & **Net** & **Post** & **All** & \(Post_{mt}\) & **All\({}_{mt}\)** \\ \hline S\_Arch\_32 & \(32\times 4\) & 6.89 G & 245 & 561 & 170 & 4512 & 232 \\ S\_Arch\_16 & \(16\times 4\) & 1.84 G & 521 & 576 & 261 & 4629 & 468 \\ S\_Arch\_8 & \(8\times 4\) & 0.62 G & 958 & 578 & 360 & 4645 & 794 \\ S\_Arch\_4 & \(4\times 4\) & 0.21 G & 1704 & 529 & 402 & 4249 & 1216 \\ \hline S\_Arch\_C\_32 & \(32\times 4\) & 5.96 G & 267 & 497 & 173 & 3992 & 249 \\ S\_Arch\_C\_16 & \(16\times 4\) & 1.80 G & 610 & 491 & 269 & 3945 & 528 \\ S\_Arch\_C\_8 & \(8\times 4\) & 0.59 G & 1048 & 497 & 337 & 3994 & 830 \\ S\_Arch\_C\_4 & \(4\times 4\) & 0.22 G & 1769 & 497 & 388 & 3995 & 1227 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Computation efficiency in FPS (SSDD). \"All\" represents the overall detection speed. When the number of channels decreases, the processing speed of the neural network stage (\"Net\") increases sharply. However, the speed of the post-processing stage (\"Post\") is more or less stable. Although when combining \"Net\" and \"Post\" together, the overall detection speed (\"All\") increases, the post-processing stage gradually becomes the new bottleneck. Post-processing takes charge of selecting a unique rectangular box for each ship detected, which has a somewhat fixed overhead. Meanwhile, this program was executed on the CPU.