# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 1 Introduction  
Maritime vessel detection plays a crucial role in maritime traffic management, resource protection, national defense, and search and rescue operations. Accurate vessel detection technology is essential for ensuring maritime safety and security. While various equipment such as cameras, radars, and satellites have been employed to address this need, their performance often suffers due to constraints imposed by weather conditions and the maritime environment. In particular, camera-based Video Surveillance Systems (VSS) face significant challenges in maintaining detection performance under diverse and adverse weather conditions. Electro-Optical (EO) cameras perform effectively in bright daytime conditions but exhibit significant performance degradation during nighttime or under adverse weather conditions. Conversely, infrared (IR) cameras enable object detection in low-light environments by utilizing infrared signals. However, their inherent limitations, such as low resolution and single-channel image output, reduce their effectiveness in detecting small vessels or distant objects. Furthermore, specific challenges posed by maritime environments, including camera instability, wave interference, and wind conditions, exacerbate the decline in detection accuracy. The recent advancements in deep learning-based image detection technology have enabled the real-time detection of objects of various sizes. Especially, the You Only Look Once (YOLO) [1] model has been successfully applied across various fields due to its high accuracy and real-time performance. However, maritime environments pose unique challenges that differ from general object detection scenarios. In open sea settings with limited background information, distinguishing between vessels and obstacles often becomes challenging. Furthermore, sunlight reflections on the water surface can obstruct the camera's view, and waves may partially obscure parts of a vessel, further complicating detection. In addition, maritime weather conditions such as fog, rain, and strong winds significantly reduce camera visibility, making it difficult to detect not only small vessels but also large ones at greater distances. These conditions often cause small vessels to be obscured by waves or large vessels to appear blurry, preventing accurate feature identification. Moreover, waves generated by the movement of large vessels can obscure nearby vessels or objects, further hindering detection efforts. The unique challenges of the maritime environment become even more pronounced in scenarios where vessels of various sizes coexist. Addressing these issues requires a novel approach that optimizes detection performance for different vessel sizes while maintaining real-time processing capabilities. This study proposes a Multi-Level Multi-Head Attention (ML-MHA) method that integrates YOLOv8's Convolutional Neural Networks (CNN)-based feature extraction with Scale-Sensitive Cross Attention (SSCA) and Self-Attention (SA). The SSCA module is designed to learn detailed positional information and features of small objects, thereby enhancing small object detection performance. In contrast, the SA module focuses on capturing global features and positional information of large objects, maximizing detection performance for larger targets. This approach provides a framework capable of detecting objects of varying sizes simultaneously while maintaining real-time performance. Additionally, to overcome the limitations posed by the single-channel nature of IR cameras, this study introduces a preprocessing technique utilizing color palettes commonly used in thermal imaging cameras. This preprocessing step transforms the monotonous IR images into richer visual representations, aiding in the clearer distinction between objects and backgrounds. As a result, the proposed approach effectively enhances detection performance even under diverse weather conditions and low-visibility environments. The contributions of this study are as follows:  
1.