## 3 Materials and Methods
### The FRCNN Method
The number of data records was increased and tried with all of the methods mentioned: F1 score, recall, and precision parameters. The inference is that the F1 score, recall, and precision were increased when the number of records increased. The maximum F1 score, recall, and precision were 97.13%, 98.32%, and 95.97%, respectively, for the proposed ACF-FRCNN method. Among the F1 score, recall, and precision parameter performances of all four methods, ACF-FRCNN had the best performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1 Score** \\ \hline ACF [17] & 84.76 & 83.76 & 84.26 \\ CNN [1] & 88.15 & 87.83 & 87.99 \\ FRCNN [8] & 94.65 & 95.32 & 94.98 \\ ACF-FRCNN & 95.97 & 98.32 & 97.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of precision, recall, and F1 score performances for different methods. Figure 12: Percentage of accuracy parameter comparison among proposed and existing methods. F1 score is treated as a better evaluation metric than accuracy since it is a harmonic mean of precision and recall. The proposed method analysis is compared with other methods and shown in Figure 13.

## 6 Discussion and Comparison

The conducted experiment used MATLAB for ship and iceberg detection. The observation was made after inference with the proposed ACF-FRCNN algorithm. The performance parameters accuracy, recall, precision, and F1 score were measured and analyzed. These performance parameters were also compared with the ACF, CNN, and FRCNN methods. The dataset used 1604 SAR images with a pixel resolution of 75 \(\times\) 75. From Figure 11 to Figure 13, it was observed that ACF combined with the FRCNN method outperformed 20% of the dataset in detecting ships and icebergs. The reason for including ACF with FRCNN was that FRCNN is only an RGB channel and is considered only a region proposal. However, ACF with FRCNN can measure the gradient magnitude and histogram. Thereby, the proposed ACF-FRCNN can detect ships and icebergs speedily and with high quality. Hence the novelty was achieved by combining ACF with the FRCNN algorithm for ship and iceberg detection and classification. The previous methods were analyzed to state the proposed method's performance. It was noticed that YOLO v3 gained an accuracy of 51.16% in detecting ship and iceberg discrimination [13]. The faster RCNN algorithm achieved accuracy for ship detection with 89.8% [32] with the SAR image dataset. The SSD introduced small ship detection accuracy of 92% and comparatively lesser accuracy than the proposed ACF-FRCNN method [33] over the SAR image dataset. Yet another CenterNet method achieved 83.71% accuracy for the SAR image dataset for detecting ships [34]. Work was introduced to detect ship detection, and it was a detection transformer (DETR) [35]. DETR achieved accuracy in detecting ships with a SAR dataset of 57.8%. After the detailed comparison, it was observed that ACF-FRCNN achieved higher performance. Table 5 lists the accuracy performance value comparison of various methods, including the proposed ACF-FRCNN method. Figure 13: Performance metric graph for detection. \begin{table}
\begin{tabular}{l l} \hline \hline
**Used Method** & **Accuracy** \\ \hline YOLO v3 [13] & 51.16\% \\ Faster RCNN [32] & 89.8\% \\ SSD [33] & 92\% \\ CenterNet [34] & 83.71\% \\ DETR [35] & 57.8\% \\ Proposed ACF-FRCNN & 96.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Various DL accuracy measure comparison with the proposed EDRL algorithm for ship and iceberg detection. The proposed ACF-FRCNN logic is a novelty since it produced optimal accuracy and faster detection of ships and icebergs over a SAR image dataset. It was also noticed that when the FRCNN was used to detect ships and icebergs directly, the FRCNN considered only color channels and missed some ROIs or added unwanted ROIs.