# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 5 Conclusions  
This paper addresses the challenges of small target detection from the perspective of UAV aerial photography by proposing an improved target detection algorithm based on YOLOv8n, called RSG-YOLO. The long-range information dependence problem is solved by introducing the receptive field coordinated attention convolution (RFCAConv), which effectively enhances the ability to detect small targets. Attention is also applied in the spatial, channel, and receptive field dimensions, allowing the model to focus more on important information while suppressing unimportant background information and reducing the impact of noise, which significantly improves target detection performance in complex backgrounds. The improved CAMSPD module retains fine-grained information about small targets during the downsampling process, enhancing feature perception of small targets. A new and more efficient basic feature extraction and fusion module, C2 GELAN, is adopted to increase the flow of gradient information while improving the feature extraction and learning capabilities of the network, thus enhancing the accuracy and robustness of the model. The characteristics of targets in the aerial view of UAVs are analyzed, and lightweighting is achieved by adding a small target detection layer and removing the large  
Figure 12: The actual scenario detection situations for each comparison model.  
target detection layer, which also improves the capability of the network to detect small targets. The improved model is shown to outperform YOLOv8n while requiring fewer computational resources and lower hardware costs, making it very friendly for deployment on low-computing-power UAV platforms.  
Although the RSG-YOLO model proposed in this paper has achieved certain results in object detection, there are still areas that can be optimized. In the CAMSPD module, due to the use of a large number of slicing operations, the amount of computation has increased, which in turn has prolonged the model training time. Subsequent research intends to make improvements from the following directions: Firstly, explore effective ways to reduce the amount of computation, focusing on improving the efficiency of the entire network training process rather than just limiting it to the inference process, so as to facilitate practical applications and deployments on different unmanned aerial vehicle platforms. Secondly, in terms of the attention mechanism, in addition to the receptive field attention currently adopted, further explore the application effects of other attention mechanisms, and strive to screen out a better attention strategy to strengthen the comprehensive performance of the model. Thirdly, focus on the research on data augmentation techniques applicable to small targets, with the hope of effectively improving the detection accuracy and ability of the model for small targets while maintaining the stability of the existing model structure, and providing more accurate and efficient solutions for object detection tasks in related fields.  
Conceptualization, Y.L.; methodology, Y.L.; software, Y.L. and B.T.; validation, B.T. and Q.G.; formal analysis, D.Z.; investigation, Y.L. and Q.G.; resources, Y.L. and D.Z.; data curation, B.T. and Q.G.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and D.Z.; visualization, B.T. and Q.G.; supervision, D.Z.; project administration, D.Z.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Research Projects of Liaoning Provincial Department of Education, China (JYTMS20230192).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.