# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 4 Results
### Qualitative Analysis  
This section presents the results of qualitative experiments on our proposed method. The ground truth is marked with a yellow detection box. The detection results of YOLOv5-L, YOLOv7, and LCAS-DetNet use green, purple, and pink detection boxes, respectively. The red ellipses in the figure indicate false detection results, and the orange ellipses indicate missed detection results.  
The detection results depicted in Figure 9 exhibit the outstanding performance of LCAS-DetNet on the HRSID dataset. Figure 9a showcases images from the dataset captured in complex scenes. The ground truth shows in Figure 9b reveals a plethora of small ships, posing a significant challenge for detection. Despite these complexities, as evidenced in Figure 9e, our method demonstrated remarkable performance in such intricate scenarios. It effectively mitigated the missed detection of target ships and reduced the likelihood of false positives. This reinforced our confidence in the ability of LCAS-DetNet to attain multi-scale receptive fields through Multi-Location Cross-Attention (MLCA). Such a design accentuates both local and global features crucial for ship detection, consequently yielding outstanding performance in ship detection tasks.  
As illustrated in the comparison of detection results in Figure 10, our proposed method maintained excellent performance on the SSDD dataset. Figure 10a,b reveal that in complex scenes, there was interference in pixel values, thereby heightening the challenge of ship detection. It became easy to overlook the detection of small boats, and even some bright noises near the shore were mistakenly identified. Nevertheless, as depicted in Figure 10e, our proposed method effectively suppressed noise interference, accurately distinguished between background and ships, and minimized the likelihood of misjudgment. We attribute this to Multi-Location Attention (MLA), which computes pixel-level correlations in channel and spatial dimensions, further combating the impact of noise on the target ship's pixel distribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **F1-Score** & **mAP (\%)** \\ \hline Faster R-CNN [20] & 81.63 & 85.31 & 0.826 & 89.63 \\ YOLOv5-S [18] & 91.15 & 91.31 & 0.91 & 94.66 \\ YOLOv5-M [18] & 91.13 & 92.36 & 0.924 & 96.21 \\ YOLOv5-L [18] & 91.83 & 93.08 & 0.923 & 96.27 \\ YOLOv7 [42] & 90.95 & 93.61 & 0.925 & 96.03 \\ FBR-Net [44] & 86.73 & 87.14 & - & 83.76 \\ SSGE-Net [45] & 88.12 & 88.97 & - & 85.63 \\ ARR-Net [46] & 89.36 & 89.73 & - & 88.42 \\ [PERSON] et al. [25] & 88.27 & 93.41 & - & 95.02 \\ YOLOv8 & 94.86 & 92.53 & 0.953 & 96.24 \\ LCAS-DetNet (Ours) & **97.52** & **96.35** & **0.969** & **98.66** \\ \hline \end{tabular}
\end{table}
Table 3: Experimental results of different methods on the SSDD dataset. We have highlighted the superior performance of each evaluation indicator in bold.  
Figure 10: The detection results of different methods on the SSDD dataset. (**a**) shows the original image, while (**b**) depicts the ground truth. (**c,d**) illustrate the detection results of YOLOv5-L and YOLOv7, respectively. Finally, (**e**) showcases the detection results of LCAS-DetNet.  
Figure 9: The detection results of different methods on the HRSID dataset. (**a**) shows the original image, while (**b**) depicts the ground truth. (**c,d**) illustrate the detection results of YOLOv5-L and YOLOv7, respectively. Finally, (**e**) showcases the detection results of LCAS-DetNet.