# UAV Image Small Object Detection Based on RSAD Algorithm
## 1 Introduction  
Unmanned aerial vehicles (UAVs) are becoming an essential component of daily life thanks to the ongoing development of UAV technology. UAVs are frequently employed in many fields because they are compact, adaptable, simple to use, and economical [1]. Drone usage is evident in both military and non-military operations. As an illustration, traffic monitoring, power patrol, locating military targets at sea, and emergency disaster aid [2; 3; 4; 5]. However, object detection is the primary technology that enables UAVs to complete these tasks. UAVs can capture aerial images in a variety of environments by carrying optical elements on their fuselage [6; 7]. Object detection technology is then used to process the images captured by UAVs and analyse the feature information in the images, which can greatly reduce the human cost and improve the monitoring efficiency. Therefore, how to process and analyse UAV images quickly, accurately and intelligently has become a research focus in the field of UAV imagery. In recent years, the break-through in computer hardware and the emergence of deep learning have led to the rapid development of object detection technology, which, as an important technology for UAV image processing, enhances the UAV's ability to understand different scenarios and can help the UAV to perform tasks in different scenarios [8; 9]. More specifically, deep learning algorithms for object detection perform significantly better than traditional methods. The feature learning framework in deep learning solves several problems that existed in traditional methods, such as generating a large number of redundant proposals through time-consuming selective search methods, as well as the manual feature design process, the selective search methods, and the manual feature design process. Recent progress of deep neural networks has significantly boosted the performance of object detection on public benchmarks such as COCO [10]. However, UAV images [11] are different from public benchmarks. Due to the UAV's large field of view, the scale of objects in UAV images varies greatly, and small objects form a significant proportion, whereas current research focuses on developing complex models to achieve high accuracy for small objects on high-resolution UAV images, which requires a lot of processing work. However, complex models are not easy to deploy on UAVs when UAV resources are limited. All these make UAV object detection more difficult than general object detection. Initial deep learning-based object detection algorithms, such as Faster R-CNN [12], Mask R-CNN [13], and YOLO [14] only perform prediction and regression in the last layer of the backbone network. This detection method is difficult to adapt to changes in the scale of objects and retains less-effective information about small objects, resulting in low accuracy of UAV image detection. The SSD [15] algorithm merges the anchor frame concept of Faster R-CNN with the regression concept of YOLO and proposes the introduction of multiple convolutional classifiers and regressors on feature maps of different scales for object detection. This method ensures robustness to scale changes and offers a new perspective for UAV image detection. Nowadays, most of the object detection algorithms based on UAV images retain the characteristics of multi-scale detection of SSD algorithm [16; 17; 18] and improve on it. [PERSON] et al. [19] proposed a global density fusion convolutional network for small objects in UAV images, which improves the model's detection rate of small objects in UAV images by fusing the feature layers of different scales, but increases the computational burden. In order to enhance the fusion efficiency of different feature layers, [PERSON] et al.