## 3 Materials and Methods
### The FRCNN Method
However, when the proposed experiment initially fed ACF followed by FRCNN, the following outcome was introduced since ACF processed the region of the proposal with the variation of the gradient and the variation of the histograms. Thus, the outcome was optimum concerning accuracy, precision, etc., and detection speed was also increased. The YOLO method is often used for moving video frame applications such as traffic on roads, surveillance video systems, etc., for SAR image datasets, and the best effect to predict objects is the combined effect of the ACF and FRCNN methods, which is a novelty. In the future, the same proposed research work will be enhanced by using a high-resolution SAR image dataset, and we will also try to implement real-time automation for detecting ships and icebergs over the SAR image dataset. ## 7 Conclusions and Future Work

Small object detection in remote sensing images such as SAR images always plays a hectic role. For the past decade, various ML and DL algorithms have been applied to detect remotely sensed objects such as ships and icebergs. Even though some non-DL and DL-based algorithms are used to see ships and icebergs, performance measures still need improvement and efficiency. The proposed research reduces this complexity in detecting and classifying icebergs and ships with DL and non-DL combinations. The ACF detectors offer regions, and the FRCNN detector works on the proposals of ACF and produces accurate classification with a small amount of complexity. The proposed method achieved better numerical performance values: loss (18.32%), accuracy (96.34%), recall (98.32%), precision (95.97%), and the F1 score (97.13%). It was finally compared with other conventional methods, and it was claimed that the ACF and FRCNN methods outperformed and reduced the detector's complexity. This framework can be extended with advanced pre-trained CNN and object detectors to include additional classes such as marine life. In the future, this proposed ACF and FRCNN algorithm will be developed with reinforcement concepts to train and automate remote sensing SAR-image-based submerged human body detection. Conceptualization, S.S.R. and S.S.; methodology, S.S.R. ; software, S.S.; validation, M.F.P.M. and S.S.; formal analysis, S.S.R. and S.S.; investigation, S.S. and M.F.P.M. ; writing \(-\) original draft preparation, S.S.R. and S.S.; writing \(-\)review and editing, M.F.P.M. and S.S.; supervision, S.S.; project administration, M.F.P.M. All authors have read and agreed to the published version of the manuscript. This research received Synergy Facade funding with grant number SFC1023. I worked with a deep learning algorithm for a ship and iceberg detection mechanism. Data Availability Statement: Data is unavailable due to privacy or ethical restrictions. The authors declare no conflict of interest. ## References

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