# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 1 Introduction  
With the rapid development of unmanned aerial vehicle (UAV) technology, target detection technology based on UAV platforms is widely applied for the recognition of dangerous targets in public places, wildlife protection, and military strikes. However, compared to target detection tasks conducted from ordinary perspectives, more severe challenges are faced from the UAV aerial perspective, including image degradation, uneven target distribution, small target sizes, and strong background noise [1]. Additionally, due to the limitations imposed on the size of the computing unit by the UAV platforms, computing power is constrained. Therefore, the development of high-precision and lightweight detection algorithms is considered essential for target detection tasks on UAV platforms. In the field of target detection, algorithms are primarily categorized into traditional target detection algorithms and target detection algorithms based on deep learning. Traditional target detection algorithms are synthesized from human-designed target featuresand machine-learning classifiers, such as Haar features [2] and directional gradient histograms [3]. For complex small target detection tasks, these methods are found to be inadequate in meeting practical needs. With the emergence of convolutional neural networks, more effective solutions for practical applications are provided by deep learning-based target detection algorithms. The existing deep learning-based target detection methods are categorized into two types according to the framework layout: one type consists of two-stage target detection algorithms based on candidate regions, such as R-CNN (Region with CNN Feature [4]), Fast R-CNN [5], and Faster R-CNN [6]. The other type includes single-stage target detection algorithms based on regression, such as the SSD (Single Shot MultiBox Detector) series [7] and the YOLO (You Only Look Once) series [8; 9; 10]. The two-stage detection algorithm is associated with high computational costs and is found to lack sufficient spatial information for small target detection, making it unsuitable for real-time small target detection. Single-stage detection algorithms are recognized for their speed and ease of deployment; however, the problem of optimizing the balance between recognition accuracy and computing speed is still faced in small target detection tasks. This issue is especially prevalent in complex scenarios, where computational overheads are caused by excessively sparse content, leading to costs that exceed the actual gains. Small target detection algorithms based on the YOLO model are widely used in UAV platforms, but the focus of research is still on achieving a balance between speed and accuracy in practical deployment. Partial Convolution (PConv) is used by [PERSON] [11] et al. to lighten the network, while SE attention is applied to improve the detection head and reduce the interference of background noise. The improved model is found to have a considerable increase in detection speed, but the accuracy improvement is limited. A shallow feature enhancement module has been proposed by [PERSON] [12] et al. to enhance the detection capability of the small target detection head, and average pooling is used to improve fast spatial pyramid pooling, which further enhances the detection performance on small targets. However, as the number of model parameters increases, the detection speed decreases. PConv is used by [PERSON] [13] et al. to lighten the network, while Equalized Focal Loss (EFL) is applied to equalize the gradient weights of the tail categories during network training, increasing the category detection ability of the network. The detection speed is effectively increased; however, the enhancement of detection accuracy for small targets is found to be limited.