## 1 Introduction  
Object detection [1] is an important task in the field of computer vision which aims to identify and locate target objects automatically in images or videos. Unlike simple classification tasks, object detection requires identifying multitargets in the image and providing a bounding box for each target to indicate its precise location. Object detection technology has been widely used in fields such as self-driving cars, video surveillance, medical image analysis, and drone image analysis [2]. In these application fields, unmanned aerial vehicle (UAV) object detection has received widespread attention in recent years, and has a significant impact on both military and civilian applications [3]. However, complex scenes, variably sized small objects, occlusions, and variable illumination bring a variety of challenges and requirements to UAV object detection technology. Therefore, research on this technology is of considerable significance in terms of improving the real-time accuracy of UAV object detection. Recently, the rapid progress in deep learning, as indicated in [4], has greatly expedited its extensive adoption in UAV object detection. Deep learning models can be broadly classified into one- or two-stage approaches, a categorization based on their algorithmic structures. The most classic two-stage models are the region-based convolutional neural network (R-CNN) algorithm series. In 2014, [PERSON] et al. [5] introduced the R-CNN, an algorithmic method of region selection, followed by classification and detection. In 2015, [PERSON] [6] improved the R-CNN and proposed a Fast R-CNN. [PERSON] [PERSON] et al. [7] made a further improvement and introduced Faster R-CNN. This further improved detection accuracy and efficiency. In 2018, [PERSON] et al. [8] introduced Cascade R-CNN. Conversely, one-stage models do not require candidate region selection. These end-to-end algorithms treat object detection as a direct regression task. Representative algorithms include the You Only Look Once (YOLO) series [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19], the Single Shot MultiBox Detector (SSD) [20], and RetinaNet [21]. These algorithms have fewer parameters, and the detection processes are considerably shorter; nevertheless, they tend to have a slightly lower accuracy than two-stage models. With continuous model development, object detection methods have evolved to meet various scene-specific needs. However, achieving real-time and highly accurate detection remains challenging, particularly because of the high flight speeds of UAVs. To address this issue, [PERSON] et al. [22] proposed a UAV detection method based on YOLOv3 and a pruning algorithm. These measures were aimed at enhancing the detection speed. The proposed UAV detection model achieved a significant reduction in model size of 95.16% and inference time of 51.37%. However, this optimization did not improve the accuracy. [PERSON] et al. [23] proposed replacing Visual Geometry Group 16 (VGG-16) with Residual Network (ResNet) specifically for UAV vehicle detection scenarios. The optimized Faster R-CNN model was effective in handling small-object vehicles while reducing false alarms and missed detections. This led to an impressive vehicle detection accuracy of 96.83%. Nonetheless, this improvement in accuracy came at the expense of decreasing the detection speed. [PERSON] et al. [24] proposed an optimized cascaded R-CNN. This enhancement involved adding superclass detection to the original algorithm. Additionally, it incorporated the fusion of regression confidence and modifications to the loss function, all aimed at enhancing object detection capability. This method effectively improves the detection performance for aerial targets. However, because of the large baseline, the model was limited by slow reasoning speed and weak mobility.