## 3 Materials and Methods
### The FRCNN Method
Figure 9 shows an example of iceberg classification, and Figure 10 shows an example of ship classification. ## 5 Performance Analysis of Results

Table 2 depicts the loss performance numerical illustration for four different methods: ACF, CNN, FRCNN, and ACF-FRCNN. This experimental loss performance was tested for the SAR dataset with an iterative method, and the number of data records was increased and tested with all methods as mentioned for the loss parameter. The inferences are that the loss gain is decreased for expanded records. The minimal loss for iteration 9 with 31.98%, 32.12%, 29.32%, and 18.32% was gained for ACF, CNN, FRCNN, and ACF-FRCNN. Among loss parameter performances, ACF-FRCNN had the best performance. Figure 10: Output of FRCNN with class of ship. Figure 9: Output of FRCNN with class of iceberg. The proposed algorithm was compared for loss performance with CNN and ACF without fast RCNN and FRCNN without aggregate channel features. The analysis of loss is explained in the graph depicted in Figure 11.

Table 3 depicts the accuracy performance numerical illustration for four different methods ACF, CNN, FRCNN, and ACF-FRCNN. This experimental accuracy performance was tested for the SAR dataset with iteration, and the number of data records was increased and tested with all methods as mentioned for the accuracy parameter. The inference is that the accuracy gain is increased for records in the dataset. The maximum accuracy for iteration 8 with 86.12%, 87.92%, 89.14%, and 96.34% was achieved for ACF, CNN, FRCNN, and ACF-FRCNN, respectively. Among accuracy parameter performances, ACF-FRCNN had the best performance. The proposed algorithm was compared for accuracy with CNN and ACF without FRCNN and FRCNN without ACF. The accuracy analysis is illustrated in the graph depicted in Figure 12. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Method** & **Iter.1** & **Iter.2** & **Iter.3** & **Iter.4** & **Iter.5** & **Iter.6** & **Iter.7** & **Iter.8** & **Iter.9** \\ \hline ACF [17] & 55.16 & 53.82 & 48.12 & 42.06 & 39.26 & 37.46 & 35.66 & 34.86 & 31.98 \\ CNN [1] & 79.23 & 76.29 & 73.65 & 70.13 & 61.43 & 52.54 & 43.31 & 34.23 & 32.12 \\ FRCNN [8] & 69.46 & 61.97 & 53.27 & 45.87 & 37.25 & 35.43 & 33.92 & 30.76 & 29.32 \\ ACF-FRCNN & 49.79 & 45.25 & 41.87 & 37.82 & 33.65 & 29.43 & 25.73 & 21.45 & 18.32 \\ \hline \end{tabular}
\end{table} 
Table 2: Loss performance comparison for different methods for SAR dataset to detect ships and icebergs. Figure 11: Percentage of loss parameter comparison among proposed and existing methods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Method** & **Iter.1** & **Iter.2** & **Iter.3** & **Iter.4** & **Iter.5** & **Iter.6** & **Iter.7** & **Iter.8** \\ \hline ACF [17] & 44.21 & 50.29 & 56.92 & 62.34 & 68.78 & 74.97 & 80.63 & 86.12 \\ CNN [1] & 49.23 & 58.32 & 62.23 & 67.39 & 70.92 & 75.78 & 81.21 & 87.92 \\ FRCNN [8] & 56.12 & 59.76 & 67.13 & 69.32 & 74.89 & 76.54 & 85.72 & 89.14 \\ ACF-FRCNN & 65.14 & 76.78 & 79.54 & 84.56 & 87.87 & 88.43 & 89.13 & 96.34 \\ \hline \end{tabular}
\end{table} 
Table 3: Accuracy comparison for ship detection over the SAR dataset with different methods. The performance metrics for the proposed system are the F1 score, recall, and precision. Here, the metrics are calculated based on the is_iceberg attribute of the given JSON dataset file. The formula used to calculate precision, recall, and the F1 score is provided in Equations (12)-(14). \[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{12}\]

\[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{13}\]

\[F1 score=\frac{2*(\text{Precision}*\text{Recall})}{(\text{Precision}*\text{ Recall})} \tag{14}\]

where TP is true positive, FP is false positive, and FN is false negative. Table 4 shows the F1 score, recall, and precision performances' numerical illustration for four different methods: ACF, CNN, FRCNN, and ACF-FRCNN. This experimental F1 score, recall, and precision performance was tested for the SAR dataset.