Analyzing the Efficacy of Synthetic Images in Unmanned Aerial Vehicle Detection

The increasing number of publicly available Unmanned Aerial Vehicles (UAVs) has underscored critical safety concerns, necessitating the development of precise detection algorithms. However, obtaining sufficient training data for these algorithms, especially for air-to-air UAV detection, remains a significant challenge. Simulation has emerged as a valuable data augmentation tool, capable of generating extensive datasets covering a diverse range of scenarios. In this study, we introduce an innovative approach to synthetic image generation, facilitating the creation of a diverse set of images to enhance the robustness of UAV detection performance. Utilizing synthetically generated images offers the advantage of automatic image segmentation and precise bounding box labeling. In this paper, We demonstrate the impact of utilizing these simulated images on algorithmic performance, emphasizing their potential to significantly enhance UAV detection capabilities.