Abstract
Accurate interpretation of temperature indicating paint (TIP) is of great importance in aviation and other industrial applications. This study presents a novel temperature interpretation approach based on the 2D spectrogram of TIP. A deep learning model called ESTNet(efficient spectrogram-temperature network) has been developed. The Gramian Angular Field (GAF) and the Continuous Wavelet Transform (CWT) are used to transform 1D reflectance spectrum into 2D spectrogram, GASF-Graph and scale2-Graph. The residual blocks and two composite coefficients are used to design RSTNet with an accuracy of 94% on KN3A samples when the input is GASF-Graph or scale2-Graph. Furthermore, RSTNet is optimized as ESTNet by the spectrogram information fusion strategy and channel attention mechanism. Using ESTNet, the interpretation accuracy of KN3A, KN6, and KN8 samples is 96%, 94%, and 96%, respectively, and RMSE is 1.5 ℃, 2.1 ℃, 1.8 ℃, respectively. This research provides valuable insight and reference for other spectroscopic applications.
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