# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 5 Results
Especially, the performance improvements were more pronounced in the IR camera environment than in the EO camera environment, demonstrating the ML-MHA mechanism's effectiveness in overcoming the low resolution and single-channel limitations of IR data. When the Ironbow and Rainbow palettes were applied, they clarified the boundary between the sea and sky, enabling the detection of some vessels previously missed in the original IR images. However, AP50 decreased to 63.2 and 63.1, respectively, compared to the Baseline model, and small object detection performance dropped to 28 and 27.5. This decline is attributed to the color variations emphasized by these palettes, which likely caused confusion during model training, leading to reduced overall detection performance. The White Hot and Black Hot palettes, on the other hand, maximized luminance contrast, making the boundary between vessels and the background more distinct. The White Hot palette achieved an AP50 of 71.1 and an AP8 of 33, improving detection performance, while the Black Hot palette recorded the highest performance with an AP50 of 73 and an AP8 of 34. The Black Hot palette, in particular, showed the most significant improvement in small object detection, contributing to overcoming the structural limitations of IR data. In contrast, the Sepia and Arctic palettes resulted in a general decline in detection performance compared to the original IR images. The Sepia palette recorded an AP50 of 60.4 and an AP8 of 26.5, while the Arctic palette showed the lowest performance, with an AP50 of 60.5 and an AP8 of 26. This is likely because these palettes failed to provide  
Figure 8: Comparison of detection results in an IR camera scene. (**a**) IR Original; (**b**) Black Hot; (**c**) Sepia. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **SSCA** & **SA** & **ML-MHA** & **Color Map** & **AP50** & **AP5** & **AP7** & **AP1** & **FPS** \\ \hline Baseline & & & & & 68.2 & 29 & 52.8 & 59.4 & 72.8 \\ Baseline+ & ✓ & & & & 71.5 & 32.2 & 53.1 & 60.5 & 72 \\  & & ✓ & & & 71.3 & 30.9 & 53.3 & 62.2 & 71.8 \\  & & ✓ & & & 72.1 & 33.4 & 54 & 63.5 & 71.4 \\  & & & ✓ & Ironbow & 63.2 & 28 & 50.2 & 56.1 & 70.9 \\  & & ✓ & Rainbow & 63.1 & 27.5 & 50.5 & 56.2 & 70.8 \\  & & ✓ & White Hot & 71.1 & 33 & 54.2 & 63 & 71.3 \\  & & ✓ & Black Hot & 73 & 34 & 55.5 & 64.5 & 71.2 \\  & & ✓ & Sepia & 60.4 & 26.5 & 48 & 54 & 71.4 \\  & ✓ & Arctic & 60.5 & 26 & 47.5 & 53.5 & 71.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study of the ML-MHA mechanism and color map in IR cameras. sufficient contrast between objects and the background, hindering the detection model's ability to improve performance. Across all settings, FPS showed a slight decrease compared to the Baseline model (72.8 FPS). However, even with the application of the ML-MHA mechanism and color palettes, the FPS remained at a sufficient level (70.8-71.4 FPS) to support real-time detection. This demonstrates that, despite the increased computational complexity introduced by the additional modules and color transformations, the model maintained reliability for real-time applications. The distribution of precision across different color palettes is presented in Figure 9 as a box plot, providing a detailed visualization of the results.