基于图像衍生的红外无人机图像仿真方法研究

Image Simulation Method of Infrared UAV Based on Image Derivation

对无人机群的红外视频进行监视是安防和军事领域的新热点。由于复杂背景下无人机图像获取难度大,图像数量难以满足相关算法的模型训练和验证等需求,因此提出一种基于图像衍生的红外无人机图像仿真方法。采用该方法对红外无人机模板图像与红外背景图像进行混合,从而生成大量不同背景下的无人机目标图像。针对图像混合技术受背景噪声影响严重、无人机目标边缘模糊和合成图像调和度低等问题,采用一种无监督的生成式对抗网络来生成调和度较高的灰度约束图像,将其与目标梯度图像作为联合约束来求解高斯-泊松方程,得到与真实图像特征一致性较高的混合图像。实验结果表明,所提方法生成的混合图像具有较高的图像调和度和视觉真实性,说明所得图像作为扩充样本可有效提高机器学习算法的性能。

Monitoring the infrared video of the unmanned aerial vehicle(UAV) group is a new hot spot in the security and military fields. Due to the difficulty of acquiring UAV images in complex backgrounds, and the number of images cannot meet the requirements of model training and verification of related algorithms, an image simulation method of infrared UAV based on image derivation is proposed. This method is used to simulate infrared UAVs. This method is used to mix the infrared UAV template image and the infrared background image to generate a large number of UAV target images in different backgrounds. Aiming at the problems of image mixing technology being severely affected by background noise, blurring of drone target edges, and low harmony of synthetic images, an unsupervised generative confrontation network is used to generate a gray-scale constrained image with a high degree of harmony. The target gradient image is used as a joint constraint to solve the Gaussian-Poisson equation, and a mixed image with high consistency with the real image characteristics is obtained. The experimental results show that the mixed image generated by the proposed method has high image harmony and visual authenticity, which shows that the obtained image as an extended sample can effectively improve the performance of the machine learning algorithm.