## 2 Related Works
### UAV Aerial Object Detection  
Object detection facilitates UAVs to quickly and accurately identify target objects and provide data support for subsequent tasks. In recent years, there has been significant research on and applications of deep learning-based methods for UAV aerial object detection, driven by the continuous advancements in deep learning technology.  
The challenge of UAV aerial object detection stems from the presence of numerous small objects in the dataset, where the objects exhibit varying scales, necessitating a detection system capable of detecting objects of different sizes. To address this challenge,Akyon et al. [29] introduced an open-source framework called Slicing-Aided Hyper Inference (SAHI) that offers a comprehensive pipeline for small-object detection. The framework incorporates slice-assisted reasoning and fine-tuning techniques. However, this two-stage detection model, which involves slicing and subsequent detection, significantly increases the detection time, making it less suitable for deployment on edge devices. For Query-Det [20], the authors proposed a design concept to improve small-object detection by introducing a small-object query mechanism. They observed that even though the deep feature map may contain relatively less information about small objects, the FPN algorithm exhibits a highly structured nature. Consequently, even on a low-resolution feature map, the approximate location of a small object can still be confidently determined. Additionally, [PERSON] et al. [4] discovered certain correlations between different feature layers. Intuitively, one can infer that the shallow layer often retains more location-related information, while the deep layer typically contains better semantic and classification-related information.  
Moreover, the difficulty of multi-scale object detection arises from the mismatch between the object's scale and the receptive field of the detection head, as well as the introduction of incomplete or redundant feature information during traditional convolutional feature extraction. The Faceboxes algorithm [30] utilizes a multi-scale prediction and joint training approach. In other words, predictions are made at different scales, and the predictions from these various scales are trained jointly. This approach involves making predictions at different scales and jointly training the predictions from these scales, thereby enhancing the algorithm's robustness and generalization ability. Similarly, the SNIP algorithm [31] is an improved version of multi-scale training, which enables the model to concentrate more on the detection of the object itself, addressing the challenges associated with multi-scale learning. Additionally, RefineDet [32] leverages the multi-layer feature map network from SSD as the RPN of Faster R-CNN, combining the strengths of both methods. Similar to FPN in feature map processing, RefineDet employs deconvolution with element-wise summation to merge deep feature maps with shallow ones, facilitating the detection of multi-scale objects.  
The SOTA research models in object detection make use of dilated convolutions to extract more comprehensive features of the object by increasing the receptive field. [PERSON] et al. initially proposed the impact of the receptive fields on objects of different scales in object detection tasks and conducted a comprehensive experimental verification to validate their observations. Leveraging the benefits of dilated convolutions in expanding the receptive field, they introduced TridentNet [33], a straightforward three-branch network. TridentNet demonstrated a significant enhancement in the accuracy of multi-scale object detection. Furthermore, [PERSON] et al. proposed a target detection model, TPH-YOLOv5 [34], for unmanned aerial vehicle scenarios. This model is based on the YOLOv5 detection framework and integrates a small-object detection pipeline and transformer structure to address the limitations of YOLO in detecting small targets and targets with drastic size variations in drone scenarios.