## 1 Introduction  
The content of satellite images consists of buildings, roads, vehicles, ships, etc. Remotely sensed imagery may combine high spatial resolution with mediocre temporal and usually only very poor spectral resolution, or encompass macroscopic, objective, comprehensive, real-time, movement, and fast elements [1], which provides new means for earth resource investigation and development [2], land improvement [3], environmental monitoring [4], and global research [5]. On the other hand, with the in-depth research of AI theory and DL technology, object detection technology has also made great progress [6]. Object detection is the use of image processing, deep learning, and other techniques to locate the object of interest from an image or video, determine whether the input image contains the object by object classification, find out the location of the target object with object localization, and frame the object, and its task is to lock the object in the image, locate the object location, and determine the object class [7]. With the intensive application of deep convolutional neural networks [8], they have become a powerful tool for object detection. As the key point of image and video understanding, object detection is the primary difficulty in solving more extended tasks, such as image segmentation [9], object tracking [10], image description [11], event detection [12], and scene understanding [13]. However, the focus of object detection differs for remote sensing images and natural images [14]. Due to the differences in imaging platforms and imaging methods, remote sensing images have complex and diverse backgrounds, large differences in object scales, and arbitrary directions, which leads to poor detection results [15]. At the same time, the resolution of many objects in remote sensing images is quite low, and the detection of small objects has long been one of the key and most difficult points in object detection [16], because small objects have the characteristics of less image coverage area, insufficient resolution, lack of accuracy in location, and inadequate feature expression. Specifically, they are more difficult to detect compared to more extended objects. Currently, the mainstream deep learning object detection algorithms are mainly divided into two categories, whose main distinction is in the presence or absence of anchors [17]. For the anchor-based approach, however, much is dependent on the number of different samples and the hyperparameters of the anchor points in terms of detection performance, and the non-maximum suppression (NMS) algorithm [18] has been introduced into the detection process to eliminate duplicate object frames, which makes the complexity and computation of the algorithm increase accordingly, limiting its popularity and any improvement in detection speed. To improve the flexibility of the detector, anchor-free methods such as CornerNet [19], FCOS [20], and CenterNet [21] have emerged and received wide attention. At present, anchorless methods have been partly studied in remote sensing image detection. [PERSON] et al. [22] proposed a structure for the feature sharing of parallel layers for aircraft objects, using an attention mechanism, and it maximizes the object characterization capability while maintaining the model complexity and detection speed. [PERSON] et al. [23] discarded the common standard convolution while using depth-separable convolution in the CenterNet residual module, which effectively reduces the network computation and redundancy. They also use an attention mechanism to suppress useless information and provide the network with stronger detection performance. [PERSON] et al. [24] used multi-scale attention FPNs in their own networks to suppress noise while enhancing the multiplexing of effective features. The GVR mechanism is used to rotate the detection frame to fit the object more closely. [PERSON] et al.