# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 1 Introduction  
Ships play a critical role in human marine activities. With the increasing growth of coastal activities, issues such as coastal transportation, national defense security, and resource protection in coastal areas have drawn significant attention [1, 2]. Against this backdrop, ship detection and recognition have become important research topics. In recent years, high-resolution remote sensing imagery has provided a crucial data foundation for monitoring and analyzing ship targets in marine environments by capturing clear surface images over vast spatial ranges at short time intervals [3, 4, 5]. Under current technological trends, deep learning-based detection and recognition methods have demonstrated strong potential. Deep learning, particularly the rise of convolutional neural networks (CNNs), has brought revolutionary changes to remote sensing ship target detection and recognition. Benefiting from their powerful feature extraction capabilities, deep learning models can automatically learn multi-level feature representations of ships, thereby effectively improving detection accuracy [4, 6, 7, 8]. Meanwhile, optimizations in ship target detection frameworks and the introduction of high-performance modelshave provided new research directions and practical references for enhancing the accuracy and practicality of remote sensing ship target detection and recognition [6; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. In recent years, numerous improved methods for ship detection and recognition networks have emerged. [PERSON] et al. [19] utilized Transformers to focus on global image features and enhanced ship target detection capabilities in SAR image datasets through pseudo-label-based feature clustering. [PERSON] et al. [20] proposed the NWD-RKA strategy to replace standard IoU-threshold-based detectors, providing sufficient supervisory information for network training and improving detection capabilities for small ships. [PERSON] et al. [21] suppressed sea clutter in SAR images using adaptive processing of frequency-domain information. [PERSON] et al. [22] introduced scattering features into SAR ship recognition tasks, fusing scattering and deep features to enrich ship feature diversity. [PERSON] et al. [23] incorporated prior knowledge of ship shapes into the network, providing shape-aligned rectangular detection boxes to overcome the limitations of traditional square convolutions, thereby improving ship recognition accuracy. For images with feature occlusion, [PERSON] et al. [24] achieved satisfactory results in classifying seven types of military ships using Gaofen-3 SAR satellite imagery through a dual-branch feature extraction and fusion architecture that separately extracts discriminative local fine-grained features and global semantic information. [PERSON] et al. [25] improved target localization in complex backgrounds by introducing an asymmetric pyramid non-local block. [PERSON] et al. [26] employed polarization self-attention and a weighted bidirectional feature pyramid network to preserve long-range dependencies and high-resolution channel-spatial features, effectively addressing ship detection challenges in sea fog scenarios. [PERSON] et al. [27] designed a Perlin noise dehazing hybrid module and a target-oriented structural similarity loss function to remove clouds and restore ship texture details, improving ship detection accuracy in cloud-interfered remote sensing images. In summary, current ship detection and recognition algorithms have made significant progress, but several challenging issues remain unresolved. Most existing models focus on detecting offshore ship targets in open seas, where ship features are distinct. However, as shown in Figure 1, in scenarios where ships are occluded by shoreline terrain, port structures, or other vessels, traditional detection techniques face significant challenges [28]. Furthermore, while some researchers have improved partial occlusion scenarios (e.g., cloud occlusion), they have not addressed general ship target occlusion.