# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 5 Results  
Table 3 compares the object detection performance of various methods using EO and IR cameras. The evaluation metrics are defined as follows: AP\({}_{50}\) measures the overall detection performance and is expressed as the average precision (%) across all vessel sizes. AP\({}_{s}\), AP\({}_{m}\), and AP\({}_{1}\) represent the precision percentages for detecting small, medium, and large vessels, respectively, providing a detailed evaluation of detection performance across different object sizes. FPS indicates the number of frames processed per second and is a key metric for assessing detection speed and real-time applicability. The parameter count, expressed in millions (M), reflects the number of trainable parameters in the model and serves as an indicator of model size and complexity. FLOPs, which denote the number of floating-point operations performed during inference, are used to evaluate computational efficiency and execution performance. These metrics were used to comprehensively compare the detection performance of models based on EO and IR cameras. Models incorporating the ML-MHA mechanism showed an overall increase in parameter count and computational complexity compared to traditional YOLO-based detection models, resulting in a slight decrease in FPS. However, detection performance improved across small, medium, and large objects. Especially, the performance enhancement in IR camera environments stands out as a major contribution of this study, effectively addressing the limitations posed by low resolution and single-channel characteristics. The initial models, SSD300 and SSD512, recorded low AP\({}_{50}\) and detailed metrics for both EO and IR cameras, highlighting their limitations in detection accuracy. SSD300 achieved an AP\({}_{50}\) of 55.1 for EO cameras and 50.3 for IR cameras, while SSD512 improved slightly to 58.3 for EO cameras and 53.5 for IR cameras. Although these models had relatively high FPS, their low detection accuracy made them less effective for real-time applications. RetinaNet outperformed SSD-based models, achieving an AP\({}_{50}\) of 60.5 for EO cameras and 55.7 for IR cameras. When ML-MHA was applied, RetinaNet's performance improved further, with an AP\({}_{50}\) of 61.9 for EO cameras and 57.2 for IR cameras, particularly in small object detection. The model also showed significant improvement in large object detection, attributed to the effectiveness of the multi-scale attention mechanism in handling medium and large objects. In the YOLO series, YOLOv4 achieved AP\({}_{50}\) scores of 62.3 for EO cameras and 56.8 for IR cameras, surpassing SSD and RetinaNet models. With the addition of the ML-MHA mechanism, small object detection performance improved, raising the AP\({}_{50}\) to 65.4 for EO cameras and 60.4 for IR cameras. YOLOv5-M achieved AP\({}_{50}\) scores of 68.3 for EO cameras and 64.1 for IR cameras. When ML-MHA was applied, the model exhibited notable improvements, particularly in medium and large object detection, resulting in AP\({}_{50}\) scores of 71.5 for EO cameras and 66.7 for IR cameras. YOLOv8-M (Base) recorded the highest performance among YOLO models, with AP\({}_{50}\) scores of 85.3 for EO cameras and 68.2 for IR cameras. The proposed ML-MHA-based model further improved detection performance across all object sizes, achieving AP\({}_{50}\) scores of 88.2 for EO cameras and 72.1 for IR cameras. These results demonstrate that ML-MHA effectively overcomes the limitations of various object sizes and the constraints of IR cameras, delivering superior detection performance overall. The proposed model's parameter count increased from 43.7 M in YOLOv8-M (Base) to 48.5 M, while FLOPs rose from 104.3 G to 118 G. This increase in complexity resulted in balanced performance improvements across the detection of small, medium, and large objects. Especially, the model achieved an AP\({}_{50}\) of 72.1 in IR camera environments, outperforming the baseline model. Meanwhile, FPS decreased slightly from 72.8 in YOLOv8-M (Base) to 71.4 in the proposed model.