# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 7 Limitation and Future Work  
This study proposed the ML-MHA structure to effectively detect vessels of various sizes, achieving significant results in EO/IR camera-based detection performance. However, there are several limitations that need to be addressed.  
First, while the proposed ML-MHA structure demonstrated excellent detection performance, it may struggle to perform optimally in resource-constrained environments such as low-quality cameras or low-power systems. Although the model achieved high accuracy through its multi-scale attention mechanism, the increased computational complexity could result in reduced detection speed and accuracy in environments like embedded systems or mobile devices. This limitation poses challenges for its applicability in real-time detection scenarios.  
Second, the data used in this study did not fully represent various maritime environmental factors, including sea fog, sunrise, sunset, and seasonal changes. These conditions have a direct impact on detection performance, and while the model performed well under specific circumstances, it faces difficulties in ensuring reliable detection across all maritime environments. The lack of datasets that account for diverse weather conditions and environmental variations remains a key factor limiting the model's practicality and scalability.  
Third, to improve detection performance in IR imagery, specific color palettes such as Black Hot and White Hot were experimentally applied. However, only a limited subset of available palettes was tested. Palette selection plays a crucial role in the detection performance of IR imagery, and the inability to automatically select the optimal palette for each scenario or to explore a wider range of palettes leaves room for improvement. Developing an optimization strategy tailored to each condition is an important area for future research.  
Future research should focus on developing lightweight model architectures that maintain real-time detection performance even in resource-constrained environments such as embedded systems and low-power devices. This can be achieved by simplifying the multi-level attention mechanism or applying low-complexity computational techniques. Additionally, it is essential to acquire a broader range of data reflecting diverse environmental factors, such as sea fog, sunrise, sunset, and seasonal variations, to enhance the model's generalization capabilities. Training on such datasets will contribute to enabling stable and reliable detection across all maritime environments.  
Furthermore, optimizing detection performance for IR imagery will require the development of algorithms capable of automatically selecting the most suitable color palette for a given situation. By introducing a dynamic selection method that evaluates the detection performance of each palette in real time and applies the most appropriate one under specific conditions, the limitations of IR data can be more effectively addressed. While the BlackHot and White Hot palettes demonstrated excellent performance, further exploration of additional color palettes that can complement or replace them is necessary to establish optimized detection strategies for various environments.  
These follow-up studies are expected to bring significant advancements in enhancing the reliability and efficiency of EO/IR camera-based detection systems, paving the way for more robust applications.