Neural network-based hybrid signal processing approach for resolving thin marine protective coating by terahertz pulsed imaging

A novel approach was presented to enhance the capability of resolving thin coating layers using terahertz pulsed imaging (TPI) based on a neural network-based hybrid signal procession method, which is of great significance for in-line painting applications. In the present work, Terahertz detected signals were obtained by numerical simulation using finite difference time domain (FDTD) method. Models of marine protective coatings with different coating structures were calculated and analyzed. Different signal pre-processing techniques, including Fourier deconvolution, Fast Fourier Transform and wavelet analysis, were employed on the terahertz signals respectively to obtain various signal features. The processed signal was subsequently adopted as the input vectors for a neural network (NN). The optimization procedure for determining the architecture of neural network was investigated and the evaluated results obtained by the different networks were compared. Furthermore, the predicted results of thinner coating layer obtained by multiple-regression analysis method and BP network prediction method respectively were compared. The analysis demonstrated that the best prediction performance was achieved by neural network technique combined with wavelet analysis. Therefore, the hybrid signal processing approach could be recommended for terahertz non-destructive testing applications of marine protective coating.