# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 1 Introduction
Other algorithms, such as Scale-Invariant Feature Transform (SIFT) [38] and Speeded-Up Robust Features (SURF) [39], also offer multi-scale feature extraction capabilities, but they may be less efficient than FPNs in terms of computational cost and handling image variations. To further enhance the detection performance of small targets, some studies have modified network connections to enable independent predictions at different feature layers without increasing the computational burden of the original model. Additionally, to address the challenges of multi-scale vessel detection, researchers have developed various enhanced feature fusion networks [40]. For example, [PERSON] et al. proposed the Dense Attention PyramidNetwork (DAPN) [41], which uses the convolutional block attention module (CBAM) to connect the top and bottom parts of the feature pyramid, extracting rich features that incorporate resolution and semantic information to solve multi-scale detection problems. Although DAPN demonstrates moderate performance in scene adaptability, it provides an effective solution for multi-scale vessel detection. [PERSON] et al. [42] developed MSSD Net, a novel detector that incorporates the FC-FPN module, an improved FPN that enhances the fusion of feature maps by introducing learnable fusion coefficients. [PERSON] et al. [43] proposed the Bi-DFFM module, which uses both top-down and bottom-up pathways to achieve higher-level feature integration, thereby improving the recognition capability for multi-scale vessels. [PERSON] et al. [44] establishes the CTF net by proposing a detection algorithm that combines convolution and transformers to improve SAR ship detection by balancing global and local features. These innovative methods offer new possibilities for improving the efficiency and accuracy of detecting vessels of varying sizes in SAR images. This detector enhances the model's ability to detect vessels of different sizes through techniques like optimizing Feature Pyramid Networks (FPN). Despite these advancements, detecting vessels in complex maritime environments, especially in dense or cluttered areas, remains a significant hurdle. Firstly, many existing multi-scale vessel recognition models achieve high detection accuracy in simple scenarios, such as individual vessels in open waters (as shown in Figure 1a), where interference factors are minimal. However, vessel targets presented in SAR images are far more varied, often moving through complex maritime environments such as offshore operations, nearshore navigation, docking states, national defense missions and civilian rescue operations. The complexity of the background significantly increases when vessels are near the coastline, particularly when multiple vessels are densely arranged (as shown in Figure 1b). This makes it difficult for most models to accurately identify each vessel, leading to missed detections. Secondly, the detection of small vessel targets is particularly challenging. During the feature extraction process, repeated downsampling often leads to the loss of feature information for small-scale targets. These targets occupy fewer pixels in the image, making it difficult for the model to capture sufficient detail. Therefore, improving the recognition capability for small-scale targets is critical for enhancing the model's performance in multi-scale vessel detection. To address the challenges in SAR ship detection, this paper introduces Small Target-YOLOv8 (ST-YOLOv8), a specialized architecture optimized for balancing detection accuracy and computational efficiency in marine environments. The selection of YOLOv8 Nano as the baseline model was validated through rigorous benchmarking against contemporary object detectors (Table 1). While Faster R-CNN demonstrates robust performance  
Figure 1: SAR images of different scenarios: (**a**) SAR images of no significant interference; (**b**) SAR images of coastal vessels with complex backgrounds.