# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 5 Results
The first column presents the detection results of YOLOv8-M (Base), the second column presents the results of the proposed model, and the last column presents the ground truth, encompassing detection outputs across different scenarios. In the detection results of YOLOv8-M (Base), large and medium-sized vessels were consistently detected. However, small vessels located farther from the camera were only intermittently detected, revealing a limitation in maintaining consistent detection. Additionally, false positives were observed, where small objects were incorrectly identified as vessels, reducing the model's overall accuracy. In contrast, the proposed model demonstrated more consistent detection of both large and small vessels compared to YOLOv8-M (Base). The detection performance for small  
Figure 5: Performance improvement comparison using the ML-MHA mechanism in YOLO models for EO and IR cameras (**Top**) EO Camera; (**Bottom**) IR Camera. vessels showed significant improvement. This enhancement can be attributed to the multi-scale attention mechanism in the proposed model, which is designed to effectively learn features of small objects and detect objects of various sizes in a balanced manner. The improved performance of the proposed model highlights its ability to reliably detect vessels of different sizes in EO camera environments, providing particularly significant advancements in small object detection compared to the baseline model. Common issues have been identified across all detection models. In daytime footage, false positives were observed, where vehicles on bridges or buoys floating in harbors were momentarily misidentified as vessels. At night, light reflections from the shoreline obscured the shapes of vessels, leading to temporary detection failures. Additionally, an analysis of detection performance under different weather conditions showed excellent performance in clear weather. However, in foggy conditions, some vessels were missed, resulting in detection \(\text{AP}_{\text{s}}\). These issues are key factors that undermine the accuracy and reliability of the models, highlighting the need for further research and improvements to achieve dependable detection across diverse environmental conditions. The results of the ablation study, which analyzed performance changes by incrementally adding the ML-MHA mechanism to the YOLO-based baseline model, are summarized in Table 4. The baseline model (\(\text{AP}_{\text{50}}\): 85.3, \(\text{AP}_{\text{s}}\): 36.8), representing the standard YOLO architecture, demonstrated reasonable detection performance across all object sizes. However, its limitations in detecting small objects were evident. Adding the SSCA module increased \(\text{AP}_{\text{50}}\) to 87.2, while \(\text{AP}_{\text{s}}\) performance improved from 36.8 to 37.5. This improvement is attributed to the SSCA module's ability to effectively learn and enhance the local features of small objects. Adding the SA module led to a slight increase in \(\text{AP}_{\text{50}}\) to 87.4, along with improvements in medium (\(\text{AP}_{\text{m}}\): 67.9) and large object (\(\text{AP}_{\text{1}}\): 81.6) detection performance. However, small object (\(\text{AP}_{\text{s}}\)) detection performance decreased slightly to 36.2, likely due to ML-MHA's goal of achieving balanced performance across all object sizes, resulting in a redistribution of focus. Ultimately, the ML-MHA-based model maximized overall detection performance (\(\text{AP}_{\text{50}}\)): 88.2 by leveraging the complementary roles of the SSCA and SA modules in detecting small and large objects, respectively. Balanced performance improvements were observed across all object sizes and although FPS decreased slightly from 67.7 in the baseline to 65.2, the overall performance improvements ensured high reliability for real-time applications. Figure 7 shows the original IR camera images, ground truth, and detection results after applying various color palettes.