# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 8 Conclusions  
This study proposes ML-MHA combining SSCA and SA modules to enhance the detection performance of vessels of various sizes using EO and IR cameras. To address the challenges of detecting small vessels, the SSCA module was introduced, achieving significant improvements in small object detection compared to existing models. The SSCA module effectively learns local features of small objects, substantially improving detection performance in low-resolution environments such as IR imagery.  
Additionally, the study leveraged CNN-based feature extraction to overcome the computational complexity and data processing limitations associated with ViT-based models. While ViT processes feature mAPs in patches to learn global contextual information, the proposed model directly utilizes local feature mAPs extracted by CNNs within the attention mechanism. This design enables more detailed and practical detection, proving especially effective in complex environments with limited information, such as IR camera footage, and demonstrating greater efficiency and practicality than ViT-based models.  
The proposed model demonstrated its ability to detect objects of various sizes effectively, with particularly strong performance in IR camera environments characterized by low resolution and single-channel data. By applying color palette preprocessing, the model overcame the limitations of IR data, showing stable and reliable detection performance under diverse environmental conditions. Among the palettes tested, the Black Hot palette proved the most effective, maximizing luminance contrast between objects and the background, and delivering superior results for detecting small vessels in IR imagery. This highlights the critical role of palette selection in influencing detection performance.  
This research not only advances applications in maritime detection using EO/IR cameras but also offers potential for broader use in other fields of image processing and object detection. The methodology presented in this study can serve as a practical and effective solution in various domains where small object detection is crucial, such as autonomous driving, medical image analysis, and space observation. For instance, it could enable the detection of small obstacles on roads in autonomous vehicles, small structures like cells or lesions in medical imaging, and detailed observations of celestial bodies in space exploration.  
Future research will focus on extending the proposed methodology to diverse datasets and application scenarios to further enhance its performance and validate the generality of ML-MHA-based detection structures. Additionally, efforts will be made to optimize real-time detection by designing lightweight model architectures and developing automated color palette selection techniques. These advancements are expected to further improve detection system performance and maximize applicability across various fields.  
Conceptualization, S.W. and B.K.; methodology, S.W.; software, S.W.; validation, B.K.; formal analysis, S.W.; investigation, B.K.; resources, S.W.; data curation, B.K.; writing--original draft preparation, S.W.; writing--review and editing, B.K. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the Sahmyook University Research Fund in 2024.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors would like to thank the members of the Future Information Network Architecture Laboratory in Korea University.  
**Conflicts of Interest:** The authors declare no conflicts of interest.