The Structrual Tuning Training Strategies for Few-Shot UAV Detection Based on YOLO

At the present stage, there are numerous excellent convolutional neural network detection models. Among them, YOLO is widely used due to its outstanding performance and effectiveness. In practical applications, existing models are often trained using data collected from real-world scenarios. However, for certain specific tasks, such as few-shot UAV data in particular scenes, overly complex models may lead to overfitting in machine learning, and direct training may not be the best approach. Based on this, and considering the need to prevent overfitting with limited data, we propose a training strategy that involves modifying the model size. This strategy specifically explores the performance of network structures under the condition and introduces optimization strategies during training. Compared to directly using and training classical models, this approach allows for more effective adjustments to the neural network structure, leading to better performance in practical cases of fewshot UAV detection. This training strategy provides a framework and paradigm for adjusting network structures in scenarios with limited data in specific applications, offering methodologies that can serve as references for model optimization and few-shot data fitting in similar situations. IATEX. This and the IEEEtran.cls file define the components of your paper [title, text, heads, etc.]. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract.