基于BP神经网络的路面红外图像仿真

Infrared Image Simulation of the Road Surface Based on the BP Neural Network

针对国内外红外图像仿真中采用热力学方法对物体温度场建模的不足,将基于BP神经网络的机器学习算法应用到物体的红外图像仿真中。通过对水泥路面的表面温度进行多次测量,得到训练样本集合,然后运用神经网络建立路面的温度场模型,并对不同气象条件设定下的路面进行仿真。根据仿真结果分析,此模型能够根据所设定的气象条件较准确地实时仿真路面的红外图像。

As to the shortcomings of the temperature field modeling based on the thermodynamics method in the infrared images simulation throughout the world, the machine learning algorithm based on the BP neural network is brought into the infrared images simulation. Through frequent testing on the temperature of the cement road surface, the training sample assemblies are obtained. Then with the neural network, the temperature field modeling of the road surface is built and the infrared images of the road surface under different set weather conditions are simulated. According to the analysis of the results of the simulation, it＇s proved that this model can simulate the infrared images of the road surface under set weather conditions more accurately in real time.