## 1 Introduction  
Lately, remote sensing rotated object detection has attracted significant research interest. This focuses on the identification and precise localization of specific objects within high-resolution satellite imagery, such as ships, aircraft, and buildings. In contrast to general object detection [1, 2], objects in remote sensing images display arbitrary orientations. In this case, using horizontal bounding boxes as the object localization result is often limited when detecting densely arranged and occluded objects. The current mainstream solution involves using oriented bounding boxes that include angle information to replace horizontal bounding boxes. Oriented bounding boxes have fewer redundant regions, making them more suitable for dense scenes. Importantly, oriented object detection preserves the directional information of the target, providing crucial data for subsequent related tasks. Recently, there has been significant progress in oriented object detection. Based on the Region Proposal, oriented detectors are categorized into two types: one-stage and two-stage oriented detectors. For instance, one-stage oriented detectors include R3 Det [3], GWD [4], KLD [5], SASM [6], S2 ANet [7], G-Rep [8], etc. Two-stage oriented detectors include RoI Trans [9], CSL [10], AOPG [11], ReDet [12], Oriented RCNN [13], Gliding Vertex [14], and LSKNet [15], etc. The success of these oriented detectors can be primarily attributed to the design of detection frameworks tailored for remote sensing objects. These oriented detectors have made improvements in various aspects, such as enhancing the representation of oriented bounding boxes, researching rotation-invariant feature extraction mechanisms, addressing the issue of feature misalignment, proposing loss functions better suited for oriented detectors, and proposing label assignment strategies better suited for remote sensing object detection. Although numerous oriented object detectors for remote sensing imagery have made significant progress, the prevailing methods still rely on ResNet [16] as the backbone network. However, this design does not fully address the specific requirements of remote sensing images. First, the basic design of ResNet assumes that the boundaries between objects and backgrounds in images are relatively clear and that image features can typically be sufficiently expressed within a local context. However, remote sensing images are typically captured from a high-resolution bird's-eye view, resulting in the presence of numerous small-sized or occluded objects, the detection of which often requires a broader contextual range and more detailed structural information. While ResNet is capable of feature extraction through deep networks, its convolution operations are confined to a local receptive field, making it difficult to capture long-range contextual information at early stages. While convolutional layers can capture some structural information, their response to small-scale and high-frequency structures is relatively weak. Due to the large stride in pooling operations and convolutional layers, detailed information (e.g., the texture and edges of tiny objects) may be lost or blurred. This makes it difficult for ResNet to capture more detailed structural information. Remote sensing images are often affected by various types of noise, making the consideration of noise robustness in detectors essential for practical applications. However, the ResNet design does not specifically account for noise interference. Particularly, when large amounts of noise are present in the image, the network may incorrectly recognize noisy regions as valid features, thereby affecting detection accuracy. Therefore, designing a backbone network specifically tailored for remote sensing image detection would contribute to improved detection accuracy. Remote sensing images are typically high-resolution bird's-eye views taken from satellites, and the image effects of such images are often affected by noise.