Emissivity measurement based on deep learning and surface roughness

Infrared stealth is an important guarantee for weapon equipment to survive on the battlefield. Emissivity is an important index to measure the infrared stealth characteristics, and the emissivity is closely related to the surface roughness of objects. Therefore, it is an important work to study the relationship between emissivity and roughness. In this paper, the correlation between emissivity and roughness is studied, and the fitting curve and specific relationship are obtained. It is found that the correlation between the emissivity in the 8–14 µm band and roughness is stronger. The cast iron surface roughness dataset is constructed, and a new convolution neural network (CNN) is designed by the feature fusion method, which is the strengthen CNN. The network can effectively extract the detail features in the image, and the model is optimized by the Adam method. Finally, the deep learning model for measuring emissivity based on the optical image is obtained. The effects of different learning rate decay methods, such as piecewise constant decay, exponential decay, cosine annealing, and cosine annealing with warm restart, on the model optimization are studied. The results show that the cosine annealing with warm restart has the best effect, the error of the model is the smallest, and its mean square error is only 0.0014. This paper presents a new idea for the emissivity measurement, which is of great significance to emissivity measurement, infrared stealth, and infrared detection.