# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 5 Results
Despite this minor reduction, the model maintained high accuracy even in challenging detection scenarios such as IR camera environments, demonstrating its reliability and suitability for real-time applications. For IR cameras, it was observed that the FPS are higher compared to EO cameras. This is because IR cameras operate with single-channel, low-resolution images, enabling higher FPS than EO cameras. However, the single-channel nature makes it challenging to effectively capture detailed object features, often resulting in reduced detection accuracy. In the case of the YOLOv8 model, the IR camera achieved an AP\({}_{50}\) of 68.2 with an FPS of \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**EO Camera**} & \multicolumn{5}{c}{**IR Camera**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**FLOPs**} \\ \cline{2-2} \cline{4-2} \cline{6-11}  & **AP\({}_{50}\)** & & & & & & & & & & **(M)** & **(G)** \\ \hline SSD300 [21] & 55.1 & 19.3 & 40.2 & 50.1 & 24.5 & 50.3 & 18.2 & 35.5 & 45.5 & 27 & 25.1 & 12.8 \\ SSD512 [21] & 58.3 & 23.5 & 45 & 55 & 22.3 & 53.5 & 18.5 & 38.7 & 49.2 & 23.5 & 26 & 15 \\ \hline RetinaNet [22] & 60.5 & 25.2 & 48.5 & 60.2 & 35 & 55.7 & 20.2 & 42 & 51.8 & 37 & 34.8 & 95 \\ RetinaNet & 61.9 & 27 & 52 & 64 & 32 & 57.2 & 23.2 & 45.3 & 53.2 & 34 & 38.5 & 108 \\ +ML-MHA & & & & & & & & & & & & \\ \hline YOLOv4 [23] & 62.3 & 28 & 52.5 & 65 & 23.9 & 56.8 & 22 & 46 & 54 & 25.6 & 63.9 & 142.7 \\ YOLOv4 & 65.4 & 30.5 & 55 & 68.5 & 21 & 60.4 & 24.7 & 49 & 57.5 & 24 & 68.5 & 155 \\ +ML-MHA & & & & & & & & & & & & \\ \hline YOLOv5-M & 68.3 & 32 & 57.5 & 72 & 60.5 & 64.1 & 26.5 & 51 & 56 & 65 & 21.2 & 83.8 \\ YOLOv5-M & 71.5 & 34.2 & 61 & 75 & 58 & 66.7 & 28.3 & 54 & 58.2 & 63 & 24.2 & 92 \\ +ML-MHA & & & & & & & & & & & & \\ \hline YOLOv6 [24] & 71.2 & 34 & 60 & 75.5 & 67.2 & 65.4 & 27.5 & 52.5 & 56.8 & 60 & 20.1 & 74.5 \\ YOLOv6 & 74.5 & 35.5 & 63.5 & 78 & 63 & 69.2 & 28.8 & 55 & 59.1 & 58 & 23.8 & 86 \\ +ML-MHA & & & & & & & & & & & & \\ \hline YOLOv7 [25] & 81.2 & 36.5 & 65 & 80 & 66.2 & 67.3 & 28.7 & 53 & 58 & 68 & 36.9 & 109.8 \\ YOLOv7 & 84.2 & 37.2 & 67 & 80.9 & 63 & 70.2 & 29.4 & 53.5 & 60.1 & 64 & 41.2 & 121 \\ +ML-MHA & & & & & & & & & & & & \\ \hline YOLOv8-M(Base) & 85.3 & 36.8 & 67 & 80.3 & 67.7 & 68.2 & 29 & 52.8 & 59.4 & 72.8 & 43.7 & 104.3 \\ Propose & 88.2 & 38 & 68.2 & 82.2 & 65.2 & 72.1 & 33.4 & 54 & 63.5 & 71.4 & 48.5 & 118 \\ (Base + ML-MHA) & & & & & & & & & & & & \\ \hline DETR [26] & 77.2 & 30 & 58 & 74 & 19.5 & 63.2 & 27.5 & 47 & 58 & 21.8 & 41 & 190.5 \\ YOLOS [16] & 74.8 & 28 & 54 & 71 & 22 & 61.8 & 25 & 45 & 55 & 24 & 28 & 115 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of Vessel Detection Performance Various Models Using EO and IR Cameras. 72.8, demonstrating improved detection performance along with fast processing speed. The proposed model, while showing a slight decrease in FPS to 71.4, achieved an improved AP\({}_{50}\) of 72.1, highlighting its enhanced detection accuracy. Transformer-based models such as DETR and YOLO-S achieved AP\({}_{50}\) scores of 77.2 and 78.4, respectively, demonstrating strong performance across objects of various sizes through the use of the Self-Attention mechanism. However, their structural characteristic of processing input data in patches significantly increased computational complexity. As a result, DETR's FLOPs reached 190.5 G and YOLOS's 115 G, while their FPS were limited to 22 and 28, respectively, making them unsuitable for real-time object detection. In contrast, the ML-MHA based model proposed in this study directly utilizes CNN feature mAP\({}_{\text{s}}\) and replaces self attention with ML-MHA, reducing computational complexity while improving accuracy. Compared to DETR, the proposed model achieves approximately 40% lower FLOPs while maintaining real-time performance.