## 2 Related Work  
For target detection in UAV aerial images, deep learning-based target detection algorithms [11] are subdivided into two categories, namely, one-stage target detection and two-stage target detection. Typical one-stage target detection algorithms are You Only Look Once (YOLO) [12], where the image is processed only once to obtain the bounding box coordinates and class regression probabilities. Typical two-stage target detection algorithms are R-CNN [13], Fast R-CNN [14], Faster R-CNN [15], R-FCN [16], SPP-Net [17], NAS-FPN [18], etc. It first generates a series of candidate boxes called regions of interest through an algorithm, and then a convolutional neural network divides the regions of interest for classification. [PERSON] et al. [19] developed a lightweight convolutional neural network structure called WearNet for automatic scratch detection on contact sliding parts such as metal forming. The structure is capable of gradually extracting and learning discriminative features for surface scratch classification, which minimizes the number of network layers and parameters while ensuring high classification accuracy and recognition rate for steel surface scratches, and provides significant advantages in terms of model size and classification depth. However, the performance of the scratch detection model is different in a variety of different scenarios, and the work does not adequately assess the generalization ability of the model. [PERSON] et al. [20] proposed an improved YOLO algorithm, which improved the detection results from the perspective of model efficiency by using target frame dimensional clustering, pre-training of classification network, training of multi-scale detection, and changing the filtering rules of candidate frames, which verified the feasibility of image localization problem in target detection. However, the research data available in the existing research is small, and the workload of data sample production is difficult to justify. [PERSON] et al. [10] introduced a new lightweight convolutional technique GSConv module, and designed a single aggregation module VoV-GSCSP module to replace the ordinary bottleneck module, which improves the inference speed while increasing the detection accuracy, this method proposes a novel lightweight target detector architecture, Slim-neck, which improves the accuracy and robustness of target detection by introducing global and self-attention module into a conventional target detector. However, its introduction of the attention module increases the computational complexity and the number of parameters, which can have an impact on the real-time performance of the target detector. [PERSON] et al. [21] adding the transformer encoder module to replace traditional CNN prediction heads to better capture target features and contextual information, and introduces a self-attention mechanism and positional encoding, enhancing the model's perception of the spatial distribution and relative position of targets. This method refreshed the detection record of the VisDrone2021 dataset, but introduced additional computational and parameter complexity, which had an impact on the real-time performance and efficiency of the target detector. [PERSON] et al. [22] improved the problem of the low detection rate of small targets, such as pedestrians, by means of data enhancement to the dataset of aerial images from UAV through dense cropping and the addition of the localized attention module, which effectively improved the detection rate of small targets such as pedestrians from the UAV viewpoint. However, this method requires a large amount of annotated data for training, which poses difficulties in obtaining data for certain application scenarios. [PERSON] et al. [23]proposed a new target detection method based on a deep separable multi head network structure. This method reduces the number of network parameters while reducing the model volume, and can quickly and accurately detect small targets in drone aerial images.