# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 2 Related Work  
Research on maritime vessel detection has employed various deep learning models to minimize the impact of environmental factors such as diverse weather conditions and camera instability, while enabling real-time detection of vessels of various sizes. In particular, CNNs have played a pivotal role in numerous object detection models by effectively learning local features and demonstrating excellent performance in terms of computational efficiency and accuracy. [PERSON] et al. [3] proposes the YOLOv5-ODConvNeXt network for detecting ships in drone images, showing significant improvements in detection efficiency and precision. [PERSON] et al. [4] utilizes an enhanced YOLOv3 algorithm for detecting small-scale vessels in complex satellite images, achieving higher accuracy compared to traditional methods. [PERSON] et al. [5] introduces BGD-Net, a lightweight, anchor-free CNN designed for real-time ship detection in Synthetic Aperture Radar (SAR) images, balancing computational efficiency and detection accuracy. [PERSON] et al. [6] maximized small object detection performance using an extended feature pyramid network. In their research focused on detecting small vessels in maritime environments, the model achieved 90.8% performance based on Intersection over Union (IOU) and showed over 85% success in detecting small vessels. This resulted in a performance improvement of more than 8% compared to previous methods. [PERSON] et al. [7] designed a lightweight deep learning detector called Lite-YOLOv5 to optimize vessel detection performance in large-scale Sentinel-1 SAR images. This study employed a lightweight network structure to significantly improve small object detection performance while enabling real-time application. The model is deployable on the NVIDIA Jetson TX2 platform, maintaining performance while demonstrating high efficiency. Lite-YOLOv5 achieved an AP\({}_{50}\) of 88.7% on the Large-Scale SAR Ship Detection Dataset (LS-SSDD-v1.0), outperforming eight existing methods. [PERSON] et al. [8] proposed Light-SDNet, an improved version of YOLOv7, designed as a lightweight network suitable for vessel detection on mobile devices or embedded systems. The lightweight vessel detection network, based on YOLOv7, was specifically designed for real-time vessel detection on embedded systems and mobile devices. By incorporating partial convolution and ghostconv modules, the model significantly improved its lightweight properties while maintaining performance on the SeaShips7000 dataset of 98.5%, improving small vessel detection performance. IR cameras, despite their limitations of low resolution and single channel output, have garnered significant research interest due to their ability to provide reliable detection in low-light and adverse weather conditions. [PERSON] et al. [9] proposed an enhanced model for IR image object detection based on YOLOv5. This model demonstrated a 21% improvement in mAP\({}_{50}\) compared to YOLOv4 on the KAIST and FLIR datasets, while reducing the number of parameters by 89%. Additionally, it applied multi-scale detection and an attention mechanism to improve small object detection. The computational cost was reduced by 84%, significantly enhancing real-time detection performance. [PERSON] et al. [10] proposed an anchor-free detection algorithm designed for real-time infrared-based detection. The model outperformed YOLOv7-tiny and YOLOv5s, achieving the highest performance with 95% precision and 90.2% recall, meeting the demands for real-time performance. This approach demonstrated outstanding results, particularly in infrared vehicle detection. [PERSON] et al. [11] improved object detection performance in low-resolution and weak signal conditions by combining visual reasoning with infrared-based object detection, utilizing CNN and deep learning-based synthetic images. Specifically, to enhance object localization accuracy in thermal images, the approach achieved an accuracy of 85.2% in tasks such as person re-identification.