## 1 Introduction  
Vehicles (UAVs), as a novel and prominent sensing platform, have become increasingly significant in a variety of fields due to their ability to capture high-resolution images. Object detection based on UAV images, which aims to detect object instances of predefined categories, has become a popular research topic. However, unlike objects in natural scene datasets such as COCO [1], the objects in UAV images are characterized by large numbers with complex backgrounds. Most of these objects are small in size (Figure 1), which significantly increases the difficulty of object detection in UAV images. Meanwhile, UAV hardware is often resource-constrained, leading to an urgent need for lightweight models for fast inference. Accurate and fast object detection is a typical problem encountered in the application of UAV images. A single-shot multibox detector (SSD), which combines the regression idea of YOLO and the anchor mechanism of Faster R-CNN to perform regression in multi-scale feature maps, demonstrated promising performance in multi-scale object detection tasks, both in terms of efficiency and accuracy [2]. For multi-scale feature maps, the shallow feature is used to detect small objects, while the deep layer feature is utilized to detect large objects. This strategy improves detection accuracy because small objects retain sufficient spatial information in shallow layers, while large objects are recognized and located by deep layer features with large receptive fields. In conclusion, the SSD showed potential in multi-scale object detection tasks [3]. However, each layer of multi-scale feature maps is individually responsible for detecting its corresponding size object, which leads to the poor performance of the SSD in detecting dense small objects. One reason is that the features of small objects generated by Conv4-3 lack sufficient semantic information. Additionally, small objects also heavily rely on contextual information. Aiming to address the aforementioned problems, various strategies and methods have been explored. A feature fusion single-shot multibox detector (FSSD) extracts features from different layers and concatenates them together, followed by down-sampling some blocks to generate a feature pyramid [4]. Furthermore, attention mechanisms are introduced into the SSD structure. For instance, an attention and feature fusion SSD (AF-SSD) uses the attention mechanism to enhance or suppress the channel dimension of the feature [5]. Similarly, an improved SSD network employs a hybrid attention unit and focal loss to improve the imbalance of positive and negative samples [6]. In UAV imagery, the target typically constitutes a minor fraction of the foreground. Extensive computation of the entire foreground and background by the SSD using dense detection heads significantly impacts the efficiency and real-time capabilities of the detection process. Spatial sparse convolution (SSC) [7] only performs convolution on sparse regions via a mask, which represents a promising alternative for increasing the speed of training and testing. SSC was first applied for object detection tasks based on 3D data, such as LiDAR or RGB-D data [8]. Detection heads must handle numerous bounding boxes, especially in one-stage methods, which are generated by general 2D image detection algorithms. Several approaches have recently used SSC as a detection head to save computation costs and increase efficiency. For instance, QueryDet based on cascade sparse query initially predicts the coarse location of objects on low-resolution feature maps and then guides the high-resolution feature maps to infer the accuracy of the location based on this coarse location [9]. A fine-grained dynamic router provides three kinds of different scale features for each detection head and adopts SSC to release the efficiency of fine dynamic routers [10]. Figure 1: A visualization of objects in a sample image from VisDrone2019 (classical UAV dataset), and a comparison of objects in the UAV image and COCO datasets.