# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 5 Results
In the original IR images, challenges such as environmental factors like waves led to some vessels, both large and small, not being consistently detected. When the Ironbow and Rainbow palettes were applied, the boundary between the sea and sky became more distinct, resulting in the detection of vessels that were previously missed in the original images. However, the detection models exhibited sensitivity to color changes, leading to overfitting to irrelevant patterns and an overall decline in detection accuracy. In contrast, the White Hot and Black Hot palettes demonstrated improved performance, detecting more vessels consistently compared to the original IR images, even though a few vessels were still missed. The Black Hot palette, in particular, enhanced the contrast between vessels and the background, making it especially effective for detecting small objects. On the other hand, the Sepia and Arctic palettes resulted in an overall decrease in detection accuracy compared to the original IR images. This was likely due to these \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **SSCA** & **SA** & **ML-MHA** & \(\text{AP}_{\text{50}}\) & \(\text{AP}_{\text{s}}\) & \(\text{AP}_{\text{m}}\) & \(\text{AP}_{\text{1}}\) & **FPS** \\ \hline Baseline & & & & 85.3 & 36.8 & 67 & 80.3 & 67.7 \\ Baseline+ & ✓ & & & 87.2 & 37.5 & 67.2 & 80.4 & 66.2 \\  & & ✓ & & 87.4 & 36.2 & 67.9 & 81.6 & 66 \\  & & ✓ & & 88.2 & 38 & 68.2 & 82.2 & 65.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study of the ML-MHA mechanism in EO cameras. palettes failing to provide sufficient information for effective model learning. In conclusion, the White Hot and Black Hot palettes showed notable performance improvements in IR camera-based detection, suggesting that color palette transformations can contribute to enhancing detection performance in IR imagery. Figure 8 compares detection results for a scene in IR camera footage where three vessels are passing by. In the original IR data, one of the small vessels was consistently undetected, highlighting the challenges in detecting small objects due to the limitations of IR cameras' single-channel data and low resolution. With the application of the Black Hot palette, all three vessels were continuously detected. This improvement is attributed to the Black Hot palette's ability to provide high luminance contrast, enhancing detection performance for all vessels, including small objects. In contrast, when the Sepia palette was applied, even the largest vessel was only intermittently detected or failed to be consistently identified, and the two smaller vessels were rarely detected. This limitation is likely due to the Sepia palette's inability to suffi  
Figure 6: Comparison of detection results using EO cameras under various environment. (**a**) Day; (**b**) Night; (**c**) Sunny; (**d**) Seafog. ciently emphasize contrast between objects and the background, making it difficult for the detection model to effectively learn object features. Figure 7: Detection results of various color palettes for IR Camera. (**a**) IR Original; (**b**) Ground Truth; (**c**) Ironbow; (**d**) Rainbow; (**e**) White Hot; (**f**) Black Hot; (**g**) Sepia; (**h**) Arctic. These findings demonstrate that the choice of color palette significantly impacts detection performance in IR camera imagery. In particular, the Black Hot palette proved to be an effective tool for consistently detecting objects of various sizes. Table 5 presents the results of an ablation study analyzing changes in detection performance when applying the ML-MHA mechanism and various color palettes in an IR camera environment. Compared to the Baseline model (AP50: 68.2, AP8: 29), the model with the ML-MHA mechanism showed an increase in AP50 to 71.3 and improved small object detection performance to 30.9. This indicates that the ML-MHA mechanism effectively learns multi-scale information in IR camera environments, addressing the limitations of small object detection.