# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 2 Related Work
Furthermore, extensive research has been conducted utilizing attention mechanisms and diverse network architectures to achieve reliable vessel detection under various weather conditions. [PERSON] et al. [12] improved vessel detection performance in adverse weather conditions, such as fog and rain, using an ensemble Generative Adversarial Network (GAN). This study, utilizing the SeaShip dataset, achieved an mean average precision (mAP) of 96.3%, a recall of 95.35%, and an F1-score of 95.85%. By applying an attention mechanism and the Bi-directional Feature Pyramid Network (BiFPN), the method effectively integrated features across multiple scales, maintaining stable detection performance even under challenging weather conditions. [PERSON] et al. [13] enhanced the object detection performance of Unmanned Surface Vessels (USVs) in maritime environments under adverse weather conditions by incorporating Reverse Depthwise Separable Convolution (RDSC) into YOLOv4. This study achieved an mAP of 89.7% and demonstrated strong results in real-time detection speed. The method was designed to ensure stable detection in various conditions, such as waves, cloudy weather, and vessel movement. [PERSON] et al. [14] optimized vessel detection performance in foggy, low-visibility coastal areas through the YOLOv8s-Fog model. This study achieved an mAP of 74.4% and incorporated deformable convolution and the channel attention mechanism, allowing for improved detection of vessels with complex shapes and varying forms. [PERSON] et al. [15] significantly improved multi-scale detection performance in vessel detection using SAR images with their dense attention pyramid network. By utilizing an attention mechanism to better detect small objects, the model improved small object detection performance by over 10% compared to previous models. The overall AP reached 92.1%, with small vessel detection performance reported at 86.3%. [PERSON] et al. [16] significantly improved object detection performance using a Transformer-based model. Compared to CNN-based models like YOLO, the Transformer-based model achieved an AP\({}_{50}\) of 95.6%, representing a performance improvement of over 5%. Furthermore, experiments on large datasets successfully demonstrated the model's effectiveness in detecting large vessels. [PERSON] et al. [17] enhanced vessel detection performance in SAR images by applying an attention mechanism based on YOLOv4. This study achieved a performance of 90.8% based on IOU, improving the precision of vessel detection using SAR imagery. Despite achieving high accuracy and efficiency, these related studies still face several limitations. For instance, the detection capabilities of IR cameras are hindered by their low resolution and single channel nature, detection reliability under various weather conditions remains inconsistent, and existing models often fail to effectively detect both small and large objects simultaneously. To address these issues, this study proposes the ML-MHA method, offering a reliable and real time solution for detecting vessels of various sizes.