# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 5 Conclusions  
This study proposed a Multi-Location Cross-Attention Ship Detection Network (LCAS-DetNet) for ship detection on SAR images. The network's ability in multi-scale feature extraction and noise resistance was validated on two datasets. Through ablative experiments, the effectiveness of the proposed Multi-Location Cross-Attention (MLCA) in spatial  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **F1-Score** & **mAP (\%)** \\ \hline Basic network & 95.48 & 77.63 & 0.806 & 81.32 \\ Basic network-MLCA & 96.18 & 85.44 & 0.858 & 86.96 \\ Basic network-LOSS & 96.36 & 79.91 & 0.812 & 82.25 \\ LCAS-DetNet & **96.59** & **86.62** & **0.862** & **86.96** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments on the HRSDI dataset. We have highlighted the superior performance of each evaluation indicator in bold.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **F1-Score** & **mAP (\%)** \\ \hline Basic network & 94.86 & 92.53 & 0.953 & 96.24 \\ Basic network-MLCA & 95.69 & 96.02 & 0.965 & 98.26 \\ Basic network-LOSS & 96.33 & 95.56 & 0.961 & 97.38 \\ LCAS-DetNet & **97.52** & **96.35** & **0.969** & **98.66** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiments on the SSDD dataset. We have highlighted the superior performance of each evaluation indicator in bold.  
localization, salt-and-pepper noise suppression, and water surface ripple interference was investigated, thus further improving the ship detection accuracy. The demonstrated potential of the MLCA mechanism in ship monitoring tasks confirmed its robust adaptability to SAR image data. Future work will focus on optimizing the MLCA mechanism to further investigate the impact of correlation attention on target localization and target value classification outcomes. Additionally, we aim to extend its application to a broader spectrum of remote sensing downstream tasks.  
Conceptualization, [PERSON] ([PERSON]) and D.L.; methodology, [PERSON] ([PERSON]), D.L. and X.W.; software, [PERSON] ([PERSON]), D.L. and X.W.; validation, [PERSON] ([PERSON]); formal analysis, B.Y. and G.C.; investigation, B.Y. and G.C.; writing--original draft preparation, [PERSON] ([PERSON]) and X.W.; writing--review and editing, [PERSON] ([PERSON]); visualization, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); supervision, [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The code can be obtained at [[https://github.com/[PERSON]/LCAS-DetNet](https://github.com/[PERSON]/LCAS-DetNet)]([https://github.com/[PERSON]/LCAS-DetNet](https://github.com/[PERSON]/LCAS-DetNet)) (accessed on 18 May 2024).  
The authors express gratitude to the anonymous reviewers for their insightful comments, which contributed to the enhancement of this manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SAR & Synthetic Aperture Radar \\ LCAS-DetNet & Multi-Location Cross-Attention Ship Detection Network \\ MLCA & Multi-Location Cross-Attention \\ MLA & Multi-Location Attention \\ SVM & Support Vector Machine \\ GIoU & Generalized Intersection over Union \\ CNNs & Convolutional Neural Networks \\ CSP & Cross Stage Partial \\ ViT & Vision Transformer \\ GELU & Gaussian Error Linear Unit \\ NMS & Non-Maximum Suppression \\ SGD & Stochastic Gradient Descent \\ DW-Conv & Depthwise Separable Convolution \\ FPN & Feature Pyramid Network \\ GPU & Graphics Processing Unit \\ CUDA & Compute Unified Device Architecture \\ GloU & Generalized Intersection over Union \\ \end{tabular}