## 1 Introduction  
Ship detection has emerged as a crucial research focus in remote sensing, relevant to both civilian and military applications, such as maritime rescue, fishery oversight, and ocean monitoring. Despite considerable advancements in maritime ship detection, there remains a lack of attention toward detecting ships obscured by weather phenomena like clouds and fog [1]. Meteorological studies reveal that clouds and fog typically cover around 40% of the Earth's surface [2]. Optical remote sensing imagery is significantly impacted by these weather conditions, with cloud interference being a prevalent issue. Complex cloud formations, including thin clouds that obscure ship characteristics, can obscure the complete distribution and details of ships in remote sensing images, leading to decreased detection accuracy. Thin cloud cover often reduces the contrast between ships and their surroundings, making it difficult to discern ship features [3]. As shown in Figure 0(a), the weak visual contrast between a ship target and its background complicates detection. Additionally, clouds can partially obscure a ship's hull, further diminishing its visibility, which presents a challenge for detection algorithms that rely on clear visibility. Figure 0(b) illustrates how clouds, with their similar shapes, textures, and brightness levels to ships, can introduce noise and distortions that may be misinterpreted as ship targets, leading to false positives and missed detections. Moreover, as depicted in Figure 0(c), ships in remote sensing imagery are often small, occupying only a limited number of pixels, making the detection of weak and small targets particularly challenging. To address the impact of cloud cover on ship target detection, cloud removal algorithms are commonly used during the image preprocessing stage to improve image quality. Previous researchers have studied several methods to tackle these issues. The Dark Channel Prior (DCP) method, proposed by [PERSON] et al. [4] in 2011, is a single-image dehazing algorithm based on statistical observations of outdoor images. However, algorithms derived from this principle have inherent limitations. The DCP method estimates atmospheric light by  
Figure 1: Challenges in ship detection under thin cloud cover. (**a**) Low target–background contrast: minimal visual difference between ship and background weakens target discernibility. (**b**) Cloud–ship similarity: similar shapes, textures, and brightness cause false detections. (**c**) Small target size: ships, occupying 2–6 pixels, limit detail for accurate detection. utilizing dark channel information, which is effective in non-sky regions. However, when processing sky areas, it tends to result in over-dehazing, making the sky look unnatural or causing color distortion. Moreover, this method often leads to overcorrection in bright areas on the ground. The cloud removal performance of this method largely depends on the selection of algorithm parameters, such as the threshold for the dark channel and the estimation method for atmospheric light [5]. These parameters need to be adjusted based on specific scenarios, which limits the applicability of the method in diverse environments. Additionally, the Haze-Optimized Transformation (HOT) method [6], which is based on statistical observations of clear-sky conditions in remote sensing, effectively removes most thin clouds. However, this method may either undercorrect or overcorrect certain ground objects. In addition, by analyzing existing work, we observe that most of these methods are implemented through end-to-end supervised training [7; 8]. Typically, cloud removal modules are used as a preprocessing step for the target detection task and integrated with the detection module. These two modules are connected through feature fusion, as shown in Figure 2a. This highly integrated structure does not reduce the difficulty of each individual task.