# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 1 Introduction
The lightweight model ITD-YOLOv8 is proposed by [PERSON] [14] et al., which redesigns the backbone network based on the lightweight network GhostHGNetV2 and uses a lightweight convolution AXConv to replace conventional convolution, significantly reducing the number of model parameters and computation. However, this results in a decrease in target detection accuracy. The VBGS-YOLOv8n was proposed by [PERSON] [15] et al., which also utilizes the lighter VanillaNet to improve the backbone network and introduces a bidirectional weighted feature pyramid network to reduce information loss between network layers. The number of model parameters and computation is greatly reduced, but the accuracy improvement is found to be limited. The above methods have been found to improve the detection algorithm for UAV platforms, and some improvement has been gained in the performance of small target detection. However, shortcomings still exist, such as detection accuracy being improved at the expense of detection speed, or lightweight adaptations being made while the detection effect remains unsatisfactory. In summary, the following problems are still ignored while targeted improvements are made:  
(1) The introduced attention mechanism is usually focused only on channel information and spatial information, while important sensory field information in the deep network is ignored. This information is considered crucial for small target detection. (2) Deep networks are usually subjected to high rates of downsampling, while small targets are characterized by limited information. High rates of downsampling lead to the loss of small target feature information, increasing the difficulty of detection. (3) Edge devices such as drones are characterized by low arithmetic power, and the deployment of lightweight models with low parameter counts results in unsatisfactory detection accuracy. Additionally, the reasoning for high-precision detection models is found to be slow, making it difficult to achieve a balance between accuracy and speed. A lightweight detection model is proposed in this paper, which fuses attention and fine-grained downsampling with YOLOv8n as the base model. This model is aimed at improving the performance of small target detection from UAV viewpoints while reducing hardware resource consumption. The main work of this paper is as follows:  
(1) Aiming at the problem of complex background and strong noise interference from the UAV aerial photography perspective, Receptive-Field Coordinated Attention (RFCAConv) is introduced to focus attention on the channel, space, and receptive field. This approach enhances the model's ability to learn important areas and ignore unimportant background interference information. At the same time, the problem of strong dependence on contextual information for small targets is addressed, improving the accuracy and robustness of the model. (2) For the problem of small target information loss due to high downsampling, a finer-grained downsampling structure, SPD (Space to Depth), is used to retain more detailed information. The defects of edge-adjacent information loss and insufficient contextual information caused by the SPD operation are also analyzed. Improvements to SPD are made through average pooling and maximal pooling, which retain more detailed information, reduce noise, and preserve contextual information in larger regions. (3) The new Generalized Efficient Layer Aggregation Network (GELAN) is used to improve the basic feature extraction and fusion module C2f, forming the C2 GELAN module with richer gradient flow information. This enhancement makes the gradient propagation in the training process more effective and helps the network to propagate the learned features in the early layers, thereby enhancing the network's learning and expression ability. (4) The shallow layer of the network is found to have richer information about small targets by adding a feature layer with rich semantic information about small targets on the neck side.