# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 1 Introduction  
Maritime vessel monitoring plays a vital role in ensuring maritime safety, protecting marine resources, facilitating crew operations, and supporting rescue missions at sea. Traditionally, monitoring relied on coast guard patrols or visible light technology. However, these approaches often fail to provide effective surveillance due to the unpredictability of natural conditions, such as weather and light interference [1, 2, 3]. As a result, developingreliable vessel monitoring technologies has become an essential task not only for coastal nations but also for global research communities. Currently, vessel monitoring primarily leverages advanced technologies such as infrared imagery, optical remote sensing, and SAR imagery [4; 5; 6]. SAR, an active microwave-based imaging system, has garnered significant attention due to its ability to operate under all weather conditions, detect metallic objects, and capture terrain features. SAR's potential in maritime traffic monitoring, illegal fishing detection [7], and maritime emergency operations has been well-established in the literature [8; 9; 10]. The technology's capacity for wide-area, high-resolution remote sensing, and its all-weather, all-day, multi-angle observation capabilities, have propelled advancements in spaceborne and airborne SAR systems. However, despite the advantages, challenges persist in the effective real-time detection of vessels from SAR images. These challenges include issues such as detecting small and low-resolution targets, which may appear as mere bright spots, leading to missed detections or false positives [11; 12]. Additionally, the varying scale of ship targets in SAR images further complicates detection efforts, emphasizing the need for more effective detection algorithms and end-to-end solutions [13; 14]. The evolution of vessel detection algorithms can be divided into two main periods. Initially [15], traditional detection methods based on Constant False Alarm Rate (CFAR) [16; 17; 18] relied on expert knowledge and manually extracted features, which limited their robustness in dynamic environments [19]. Although these methods performed well on simple, singular SAR images, their effectiveness often fell short in complex maritime environments [20]. With advances in technology, Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in image and video analysis tasks [21], and are widely used in computer vision fields such as semantic segmentation [22], object detection [23], and image description [24]. CNNs can automatically learn stable features of objects from large datasets, reducing reliance on manual feature extraction and enhancing model robustness. Therefore, in the task of vessel detection in SAR images, CNN-based detection algorithms offer significant advantages over traditional methods. Recent advances in deep learning have driven the development of two categories of CNN-based vessel detection: two-stage detectors and single-stage detectors [25; 26; 27]. Two-stage detectors, such as Faster R-CNN [28], Libra R-CNN [29], and Mask R-CNN [30], identify objects through two separate classification and regression processes. In contrast, single-stage detectors, such as the YOLO series [31; 32; 33], SSD [34], and FCOS [35], use fully convolutional networks to simultaneously perform classification and regression tasks in an end-to-end manner [36]. This gives them a speed advantage over two-stage detectors, although the latter may have an edge in accuracy. Due to the vast resolution differences in SAR images, where the largest and smallest pixel areas in the same dataset can differ by nearly a thousandfold, detection models need to handle multi-scale targets effectively. Feature Pyramid Networks (FPNs) [37] are one effective solution to this issue. They extract and represent features hierarchically, efficiently handling images of varying sizes and resolutions.