## 2 Related Work
### Weak Target Ship Detection  
Recently, deep learning algorithms have gained prominence in ship target detection within remote sensing images. These neural network models, fueled by extensive labeled datasets, automatically learn features that enhance target detection performance, surpassing traditional methods [10]. Unlike conventional detection techniques, which depend on predefined thresholds or knowledge of sea clutter distribution, deep learning approaches are data-driven, offering innovative solutions for diverse scenarios [11]. Popular object  
Figure 2: Illustration of module connections in detection frameworks. (**a**) One-stage framework: cloud removal and detection modules fuse features directly. (**b**) Multi-stage framework: each module has separate supervision, lacking inter-module information exchange. (**c**) Hierarchical supervised framework: TCR and STD modules are integrated with hierarchical supervision for enhanced ship detection in thin clouds. detection algorithms include DetectorNet [12], SSD [13], and the YOLO series [14; 15; 16; 17]. [PERSON] et al. [18] were pioneers in applying the R-CNN method for detecting airplane targets in complex high-resolution remote sensing images, achieving results significantly better than traditional techniques. This breakthrough spurred rapid advancements in deep learning-based object detection for remote sensing applications. [PERSON] et al. [19] developed a rotation-invariant CNN, the RICNN model, which augments the RCNN framework [20] by incorporating a rotation-invariant layer to bolster the backbone network's capability in extracting features from remote sensing targets, utilizing architectures like AlexNet [21]. [PERSON] et al. [22] utilized the Faster R-CNN framework along with ResNet [23] for feature extraction to propose a Position-Sensitive Balancing (PSB) framework. This approach aims to reconcile translation invariance during the classification phase with translation variability in the detection phase. [PERSON] et al. [24] developed a feature-attention object detection framework that improves performance in remote sensing images by focusing on learning intrinsic representations from various perspectives in an end-to-end architecture. In [25], a novel convolutional neural network called YOLOrs was introduced, specifically aimed at real-time object detection in multi-modal remote sensing images. This method is adept at detecting objects across multiple scales, leveraging a smaller receptive field to better interpret small targets and predict object orientation. Additionally, ref. [26] presents a CNN model designed to extract deep features for identifying potential ship targets in both offshore and nearshore environments. Due to long-range imaging characteristics and sensor hardware limitations [13; 27], remote sensing images often exhibit low ground resolution for individual pixels, with many targets appearing as small objects. Small-scale objects occupy fewer pixels, limiting the information available for extracting key discriminative features [28]. Furthermore, deep learning methods struggle to derive robust representations from the limited pixel data of these small targets, leading to poorer performance in small object detection compared to medium- and large-scale targets. Thus, enhancing small object detection has become a pressing concern [29; 30]. [PERSON] et al. [31] introduced a multi-vision small object detector capable of quickly and accurately identifying airplanes, cars, and ships in remote sensing images. They implemented a multi-scale residual block (MRB) using dilated convolution in cascaded residual blocks to capture multi-scale contextual information. [PERSON] et al. [32] tackled the issue of small objects being lost in deeper layers of a top-down architecture by proposing a novel multi-scale detection network (MSDN) that preserves the resolution of deep features and improves the representation of multi-scale object features. Additionally, [PERSON] et al.