# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 4 Results  
### Quantitative Analysis  
We chose the above evaluation indicators as quantitative indicators to evaluate network performance. Among them, precision represents the model's capability to correctly detect ships, while recall represents the model's ability to capture all existing ships. The F1-score index and mAP are used together as a comprehensive evaluation index to assess the overall performance of the model.  
On the HRSID dataset [31], our method exhibited superior performance in ship detection when contrasted with other advanced target detection algorithms, for example, Faster R-CNN [20], YOLOv5 [18], YOLOv7 [42], RetinaNet [43], CSD-YOLO [26], the work of [PERSON] [25], and YOLOv8. Table 2 shows the experimental results of different methods on the HRSID dataset. The evaluation metrics precision and recall achieved 96.59% and 86.62%, while the overall assessment metrics F1-Score and mAP were 0.862 and 86.96%, demonstrating excellent performance compared to other networks. Precision showed an improvement of 1.16-17.61%, recall increased by 2.44-19.36%. Moreover, both F1-Score and mAP outperformed the results achieved by other networks. The quantitative results on the HRSID dataset indicated that only 3.41% of non-ship objects were incorrectly identified as ships. This suggests that the proposed LCAS-DetNet demonstrates remarkable potential in practical ship detection applications. Only a very small number of interfering noise is mistaken as ship hulls, which greatly enhances the reliability and practicality of the system.  
On the SSDD dataset [32], we compared Faster R-CNN [20], YOLOv5 [18], YOLOv7 [42], FBR-Net [44], SGE-Net [45], ARR-Net [46], the work of [PERSON] [25], and YOLOv8 to our LCAS-DetNet. Table 3 shows the experimental results of different methods on the SSDD dataset. Our method demonstrated improvements in SAR ship detection. The precision value of LCAS-DetNet reached 97.52%, showing an improvement of 2.81-19.46% compared to other methods. The recall value reached 96.35%, showing an improvement of 4.21-12.94%. In the overall evaluation, the F1-Score reached 0.969, and the mAP reached 98.66%. The outstanding performance of the proposed network on the SSDD dataset further confirms its practical potential. The 96.35% recall rate indicates that in actual prediction results, only 3-4 ships out of every 100 are missed, providing a more reliable solution for the real-time monitoring of maritime ships.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **F1-Score** & **mAP (\%)** \\ \hline Faster R-CNN [20] & 88.81 & 72.57 & 0.798 & 77.98 \\ YOLOv5-S [18] & 89.57 & 77.37 & 0.826 & 84.94 \\ YOLOv5-M [18] & 91.07 & 78.06 & 0.828 & 84.74 \\ YOLOv5-L [18] & 91.88 & 78.83 & 0.837 & 85.01 \\ YOLOv7 [42] & 92.09 & 78.02 & 0.844 & 85.98 \\ RetinaNet [43] & 82.16 & 83.35 & - & 81.14 \\ CSD-YOLO [26] & 93.22 & 80.42 & - & 86.10 \\ [PERSON] et al. [25] & 84.37 & 84.56 & - & 85.11 \\ YOLOv8 & 95.48 & 77.63 & 0.806 & 81.32 \\ LCAS-DetNet (Ours) & **96.59** & **86.62** & **0.862** & **86.96** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of different methods on the HRSID dataset. We have highlighted the superior performance of each evaluation indicator in bold.