A multi-illumination visible light UAV image simulation method based on attention mechanism

With the rapid development of the civilian drone market, the phenomenon of illegal drone flights is escalating, making regulation increasingly challenging. To safeguard public safety, the tracking and countermeasure technology for drones has become an urgent priority. However, due to various constraints, obtaining specific drone images in complex backgrounds is challenging, especially under different lighting conditions. Additionally, the number of available images is insufficient to meet the training and validation needs of tracking algorithms and other requirements. In this context, this paper, inspired by the attention mechanism, proposes a visible light UAV image simulation method based on Generative Adversarial Networks (GANs). This method transforms physical simulation images generated by computers into drone images with authentic styles under different lighting conditions. Subsequently, the Mask R-CNN image segmentation model is employed to accurately separate the generated drone targets from the original background, and the Poisson blending technique is used to seamlessly merge them with different backgrounds. Experimental results indicate that our approach generates drone images with features closer to real images across various evaluation criteria. Furthermore, after merging with different backgrounds, the images exhibit high coherence and visual realism, providing a viable solution for effectively expanding the datasets of drone tracking algorithms.