# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 6 Discussion  
This study proposes ML-MHA combining the SSCA and SA modules with the YOLOv8 framework, utilizing EO/IR cameras to rapidly and accurately detect vessels of various sizes in maritime environments. Particular emphasis was placed on improving small object detection performance in IR imagery, which is characterized by low resolution and single channel data--a significant contribution aimed at addressing persistent issues in existing detection models.  
The proposed model effectively detected vessels of various sizes through its multi-level attention mechanism. Detection performance improved from 85.3 to 88.2 AP\({}_{50}\) in EO cameras and from 68.2 to 73 AP\({}_{50}\) in IR cameras (when using the Black Hot palette). This demonstrates the significance of applying an attention mechanism to the CNN feature mAP\({}_{\text{s}}\) for vessel detection. The model exhibited superior performance not only in EO camera environments but also in IR camera settings, where distinguishing between background and objects is challenging due to low resolution. These findings underscore the advantages of the proposed model and the selected color palettes in enhancing detection performance in such scenarios.  
Experiments with various color palettes revealed that the Black Hot and White Hot palettes provided high luminance contrast, effectively improving small object detection performance. The Black Hot palette achieved the highest performance with an AP\({}_{50}\) of 73 and an AP\({}_{\text{s}}\) of 34, demonstrating its ability to overcome the structural limitations of IR imagery. In contrast, the Ironbow and Rainbow palettes clarified the sea and sky boundaries but resulted in reduced detection performance (AP\({}_{50}\): 63.2, 63.1). Sepia and Arctic palettes showed a general decline in detection performance (AP\({}_{50}\): 60.4, 60.5), highlighting the  
Figure 9: Box plot of AP by color palette.  
importance of selecting appropriate color palettes and their positive impact on IR data detection performance.  
Unlike Transformer-based approaches, the proposed methodology optimizes model performance by directly utilizing CNN feature mAP\({}_{\text{s}}\). The ViT approach divides feature mAP\({}_{\text{s}}\) into patches, increasing computational complexity and limiting efficiency in IR image environments with restricted information. By directly applying the CNN-extracted local feature mAP\({}_{\text{s}}\) to the attention mechanism, the proposed model better captures fine-grained features. This is particularly effective and practical for IR images, which are characterized by low resolution and limited information, offering a significant advantage over ViT.  
Although the FPS of the proposed model decreased slightly compared to YOLOv8-M (Base) (72.8 to 71.4), it maintained real-time detection capabilities while achieving high accuracy, making it highly competitive for practical maritime monitoring applications. Especially, the ML-MHA combined with the Black Hot palette demonstrated more pronounced performance improvements in IR camera environments than in EO camera settings, proving its effectiveness in overcoming the limitations of low resolution and single-channel IR imagery.