# UAV Image Small Object Detection Based on RSAD Algorithm
## 2 Related Work  
### Object Detection in UAV Images  
Object detection was initially applied in the detection of general images. It is usually classified into two main categories, two-stage object detection algorithms and one-stage object detection algorithms, and they differ in their strengths and limitations.  
The two-stage algorithm is known for its high detection accuracy, such as the Faster R-CNN mentioned above. It first generates the proposed region from the image and then classifies and localises the objects in it. However, for UAV images, its detection accuracy is drastically reduced. To address this phenomenon, many scholars have conducted research. [PERSON] et al. [22] use VGGNet and ResNet as the feature extraction networks in Faster R-CNN to detect maize tasks, both with images from UAV. They modified the anchor size in the region proposal network to match the real size of the tassel pixels, which improved the detection accuracy. However, detection efficiency has a long way to go. The ROI transformer, developed by [PERSON] et al. [23], produces rotational ROIs using RPN from horizontal ROIs. This considerably improves the detection accuracy of oriented objects. However, the network became heavy and sophisticated due to fully linked layers and a ROI alignment mechanism that rotated ROIs. For UAV images, a label-oriented object format called gliding vertexes was introduced by [PERSON] et al. [24] and acquired four vertex gliding offsets on the regression branch of the Faster R-CNN head. It can accurately detect multi-directional objects, but it also introduces a certain amount of computation and increases the computational burden.  
One-stage object detectors are efficient but usually have lower accuracy, such as SSD mentioned above. The core of the one-stage object detection algorithm is regression, which eliminates the region proposal aspect of the two-stage method; there will be no preclassification or regression process, and the specific categories are directly classified and regressed against the boxes [25]. [PERSON] et al. [26] achieved real-time detection of UAV images by performing target frame clustering analysis, pre-training the model, employing multi-scale training, and optimizing the filtering rules of candidate frames for the YOLO algorithm from the perspective of detection efficiency. However, the detection accuracy is slightly lower. Inspired by YOLO architecture, [PERSON] et al. [27] propose a novel single-stage detection architecture. [PERSON] et al. [28] used SSD as a baseline network and improved SSD using feature enhancement modules such as super-resolution, inverse plethysmography and feature fusion. These improvements effectively enhanced the detection accuracy of SSD for UAV images, but at the expense of a significant amount of detection speed. [PERSON], [PERSON] et al. [29] designed a multi-block SSD mechanism consisting of three steps to address the limitations of small object detection for UAV surveillance of railway scenes. The original input image is first divided into four overlapping blocks. The next step is to send each block to an SSD independently for detection. The overlapping frames of the sublayers are then eliminated using non-maxima splicing and integration of the detection results from the four blocks. This can lower the leakage rate and considerably increase the detection accuracy of small objects in UAV images. However, it decreases the detection efficiency and makes the model more complex.