Small Unmanned Aerial Vehicle Detection Based on DetUAV-Mamba Model

In this paper, we propose a small UAV detection system based on the DetUAV-Mamba model, which integrates a state-space model into the YOLO framework to improve the identification of small targets. The DetUAV-Mamba model is designed for high-resolution images and enables fast and accurate detection of micro-UAVs, which is crucial for modern security applications. By combining Mamba with an attention mechanism, the model significantly improves convergence speed and detection accuracy. Experimental results show that the DetUAV-Mamba model outperforms other models on both the UAV tracking dataset and the UAV410 dataset, demonstrating its potential for real-world aerial surveillance.