Aibibu Tuerniyazi, Lan Jinhui, Zeng Yiliang, Hu Jinghao, Yong Zhuo
Department of Instrument Science and Technology, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Xinjiang Vocational and Technical College of Communications, Urumqi, 831401, China.
Sci Rep. 2025 Feb 12;15(1):5254. doi: 10.1038/s41598-025-89585-x.
With the rapid development of infrared (IR) imaging UAV technology, infrared aerial image processing technology has been applied in different fields. But it is not very convenient to obtain real aerial images in some cases because of flight limitations, acquisition costs and other factors. So, it is necessary to simulate UAV infrared images by computer. This paper proposed an improved infrared aerial image simulation method based on open source AirSim. By improving the original AirSim infrared image simulation method, the simulation quality of the infrared image is improved via 3-dimensional segmented model processing. The infrared aerial images of the traffic scene with different viewing angles are simulated via the proposed method in this paper and we constructed infrared traffic scene simulation dataset (IR-TSS) containing seven types of objects. We propose the efficient EfficientNCSP-Net net for the IR-TSS dataset and use popular methods for comparative experiments. The experimental results show that the proposed EfficientNCSP-Net has an mAP greater than 96% for object detection on IR-TSS dataset, which is better than those of the existing methods. This paper not only contributes to research on infrared image simulations of traffic scenes, but also has referential significance in other aerial image simulation fields.
随着红外（IR）成像无人机技术的快速发展，红外航空图像处理技术已在不同领域得到应用。但由于飞行限制、采集成本等因素，在某些情况下获取真实航空图像并不十分方便。因此，有必要通过计算机模拟无人机红外图像。本文提出了一种基于开源AirSim的改进型红外航空图像模拟方法。通过改进原始的AirSim红外图像模拟方法，经三维分割模型处理提高了红外图像的模拟质量。利用本文提出的方法模拟了不同视角的交通场景红外航空图像，并构建了包含七种类型物体的红外交通场景模拟数据集（IR-TSS）。针对IR-TSS数据集，我们提出了高效的EfficientNCSP-Net网络，并采用流行方法进行对比实验。实验结果表明，所提出的EfficientNCSP-Net在IR-TSS数据集上进行目标检测时的平均精度均值（mAP）大于96%，优于现有方法。本文不仅有助于交通场景红外图像模拟的研究，在其他航空图像模拟领域也具有参考意义。
