## 2 Related Works  
Object detection algorithms are typically categorized into traditional algorithms and deep learning-based algorithms. Traditional object detection algorithms (e.g., SIFT [1], HOG [2], and DPM [3]) mainly rely on hand-crafted features. These methods lack robustness when dealing with scale and viewpoint variations. Deep learning-based object detection algorithms can be divided into two main categories: one is region proposal-based two-stage algorithms, such as the R-CNN series [4; 5; 6; 7]. These algorithms produce candidate boxes and employ convolutional neural networks (CNNs) to classify them. However, the slow processing speed of two-stage methods limits their effectiveness for real-time UAV image processing. The other category is one-stage algorithms, such as the YOLO series [8; 9; 10; 11; 12; 13; 14; 15; 16], SSD [17], and RetinaNet [18], which directly predict object categories and bounding box coordinates through a single neural network model, without the need to generate candidate regions first, as in two-stage algorithms. These methods typically offer higher inference speeds but sacrifice detection accuracy to some extent. Additionally, [PERSON] et al. [19] introduced the DETR model by incorporating the Transformer architecture. Specifically, DETR encodes the input image and the target set into two sets and then predicts the class, position, and count of objects by matching these two sets. This transformation simplifies the detection process and eliminates the need for post-processing steps. However, from UAV perspectives, objects are typically small in size, making it challenging to directly apply the previously mentioned detection algorithms to UAV aerial scenarios. In recent years, many research efforts have emerged to tackle challenges in UAV aerial object detection. Regarding target localization and recognition in UAV images affected by adverse weather, [PERSON] et al. [20] introduced ODFC-YOLO, integrating image dehazing with object detection to enhance detection precision. However, further research is needed to optimize model parameters and computational efficiency. To meet the demands of small object detection in UAV images under complex interference backgrounds while balancing detection accuracy and processing speed, [PERSON] et al. [21] designed the Drone-DETR algorithm based on the RT-DETR framework. This algorithm integrates a small object detection module and a feature fusion attention module, achieving a perfect balance between model lightweighting and high performance. However, its detection performance degrades under extreme lighting conditions (such as overexposure or low-light environments). [PERSON] et al. [22] proposed an approach based on RetinaNet, which optimizes the scale and number of anchors through a systematic clustering method and refines focal loss computation; however, it suffers from slower speeds. [PERSON] et al. [23] embedded the STC structure into YOLOv8 to enhance performance for small object detection in UAV images, achieving significant performance improvements but leading to a notable rise in the number of model parameters. [PERSON] et al. [24] proposed the Bi-PAN-FPN framework to improve the model's capability in detecting small objects. However, it neglects the limited small object features present in deeper feature maps, which affects overall detection accuracy. [PERSON] et al. [25] proposed a feature processing module called FFNB, which fully integrates shallow and deep features, improving model recall. At the same time, deep convolution is used to reduce model parameters, but the complexity of the model leads to a decrease in real-time performance. [PERSON] et al. [26] designed the C3D module to replace the C2f module in YOLOv8, aiming to preserve more comprehensive information, but it increases the computational complexity. [PERSON] et al. [27] added a high-resolution detection branch to the YOLOv8 detection head and used both SPD Conv and GAM attention mechanisms to improve the detection accuracy of small targets.