Siamese network-based spectral reconstruction for rapid identification of fire-retardant coatings

Abstract

This paper proposes a new method to improve the accuracy of analysing low-quality spectral data, namely twin spectral reconstruction network. It consists of two neural networks with shared weights and generates useful spectral fingerprints in low-quality spectra by learning from high-quality spectra. The proposed method is tested on a new and challenging task of identifying fire-retardant coating (FRC) brands using low-quality spectra under small sample conditions. It significantly improves the identification accuracy compared to the baseline classifiers, and the reconstructed high-quality spectra closely resemble the target spectra. In addition, this paper presents a low-cost approach for FRC identification using smartphone videos and machine learning. It records short videos of samples being illuminated by a colour-changing screen and converts them into spectral data. As a pre-screening tool, it yields an accuracy of 87 % and can greatly reduce the cost and complexity of FRC identification compared to baseline techniques.