# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 5 Results
Furthermore, it delivers superior small object detection, demonstrating a more efficient and practical solution than transformer-based models in terms of balancing accuracy and real-time applicability. Figure 5 shows the comparison of detection performance for EO and IR cameras after applying the proposed ML-MHA mechanism to YOLO models. In the EO camera environment, the application of the ML-MHA mechanism resulted in significant improvements across all key performance metrics. Especially, small object detection performance showed meaningful enhancements, increasing from 28 to 30.5 for YOLOv4 and from 32 to 34.2 for YOLOv5. These improvements are attributed to the ML-MHA mechanism's ability to effectively learn features of small objects, thereby enhancing the overall performance of the detection model. On the other hand, marginal improvements were observed in the detection of medium-sized and large objects. This is likely because existing YOLO models already demonstrate high performance in detecting medium and large objects, leaving little room for further enhancement. The proposed model (Base + ML-MHA) showed an increase in AP\({}_{50}\) from 85.3 to 88.2 and in small object detection from 36.8 to 38 compared to YOLOv8-B (Base), confirming overall performance improvements. These results suggest that ML-MHA effectively learns features of objects of various sizes through its multi-scale attention mechanism. It especially addresses the limitations of YOLOv8 in small object detection, delivering better detection performance in EO camera environments despite the already strong baseline performance of YOLOv8. In the IR camera environment, the application of the ML-MHA mechanism resulted in even more pronounced performance improvements compared to the EO camera environment. In particular, small object detection performance showed significant enhancements, with YOLOv5-M increasing from 26.5 to 28.3 and YOLOv7 improving from 28.7 to 29.4. These improvements highlight the module's ability to effectively learn multi-scale information, addressing the limitations of IR cameras, which provide restricted single-channel information that makes detection challenging. These results demonstrate that the proposed ML-MHA mechanism has a relatively greater impact in IR camera environments than in EO camera environments. The proposed model (Base + ML-MHA) demonstrated overall performance improvements compared to YOLOv8-B (Base) in both EO and IR camera environments. In the EO camera environment, AP\({}_{50}\) increased from 85.3 to 88.2, while small object detection performance (AP\({}_{\text{s}}\)) improved from 36.8 to 38, indicating evenly distributed enhancements over the baseline YOLOv8. Especially, the model achieved positive performance gains not only for small objects but also for medium and large objects. In the IR camera environment, the performance improvement of the proposed module was even more pronounced compared to the EO camera environment. AP\({}_{50}\) increased from 68.2 to 72.1, and AP\({}_{\text{s}}\) increased significantly from 29 to 33.4. These results indicate that the ML-MHA mechanism effectively compensates for the low resolution and limited information of IR camera data. By leveraging multi-scale interactions, ML-MHA enhancesthe features of small objects and facilitates clearer differentiation between objects and backgrounds in IR imagery, leading to substantial detection improvements. These findings demonstrate that the proposed ML-MHA-based model successfully addresses the challenges of small object detection in IR camera environments and enables reliable detection even under constrained data conditions. Furthermore, the model's consistent performance improvements in both EO and IR camera environments highlight its potential as a practical detection solution adaptable to various maritime scenarios. Figure 6 compares the detection results using EO cameras under various environment, including day, night, sunny, and seafog conditions.