## 4 Results and Discussion
### Individual Stage Performances  
The input of each stage is fed with entirely true samples while calculating the individual stage performances. For instance, only images featuring ship(s) are used in the localization stage. Only the navy class ships retrieved with the ground truth bBox are provided in the identification stage. The individual stage results are summarized in Table 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline Detection & 98.59 & 99.81 & 98.53 & 99.17 \\ Localization & 89.04 & 91.04 & 97.60 & 94.20 \\ Recognition & 84.31 & 72.57 1 & 78.33 1 & 75.34 1 \\  & & 84.30 2 & 83.85 2 & 84.08 2 \\ Identification & 80.99 & 83.67 1 & 86.63 1 & 85.12 1 \\  & & 80.98 2 & 83.31 2 & 82.13 2 \\ \hline \hline \end{tabular}  
* Arithmetic mean value; 2 Class sample weighted mean value.  
\end{table} 
Table 7: The performance of the individual stage (values in percentage). Figure 3: (**a**) Navy ship, (**b**) PFMO applied navy ship; (**c**) Yacht, (**d**) PFMO applied yacht. The results showed that the accuracy decreases according to the level of the stage; therefore, details increase. The accuracy of the detection phase is very high despite the complex backgrounds. More importantly, the recall value for the next stage shows promise for avoiding missing any ship(s) images. The confusion matrix of the detection stage is given in Table 8.  
The localization stage also presents precise outputs, but the relatively lower recall value causes some of the ships to be missed, thus resulting in lower accuracy in the following stages. The confusion matrix of the recognition stage is given in Table 9. The barge-pontoon, the container, the general cargo, the LPG, the navy, the roro, the small boat, and the tanker classes have close accuracy values to the overall accuracy of the recognition stage. On the other hand, the coaster, the drill, the ferry, the floating dock, the offshore, the passenger, and the \"undefined\" classes illustrate lower accuracy values than the overall value. When the less accurate classes are analyzed, the following outcomes are deducted:  
* The coaster class consists of small water tankers, ore carriers, or cargo ships, which contain small ships that can be confused with small boats, as in this study. In particular, relatively low-resolution images do not include precise details and fail to detect compelling features of these small ships. * Even though the number of ships in the drill ship class is minimal, the accuracy is close to the overall accuracy. Additionally, the drill class features are very similar to the barge-pontoon class. The drilling instruments in a drill ship are generally located on a barge-pontoon. * The floating dock class is another class with limited sampling, illustrating a mixture with the barge-pontoon due to the similar shape form and the navy classes since floating docks carry navy ships in the test set. * The offshore ship class has two characteristics: the navigating bridge on the bow side of the ship and a high bow freeboard. It seems that the number of samples in this class and the level of the differential features wasn't enough to perform over the overall value. * Some big yachts in the small boat class can be confused with passenger ships. A decline in accuracy regarding the passenger class relative to the overall results from this variety can be noted. * The \"Undefined\" class comprises ships with different features from the defined classes or having a pure resolution that makes them impossible to be classified. Most ships in the \"undefined\" class do not have common features to be learned by the network. Thus, the low accuracy in this class is an expected result. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{3}{c}{**Predicted Class**} \\ \cline{3-4}  & & **Non-Ship** & **Ship** \\ \hline \multirow{3}{*}{**mini**} & Non-ship & **92** & 8 \\  & Ship & 0.2 & **99.8** \\ \hline \hline \end{tabular}
\end{table}