Discrimination of Steel Coatings with Different Degradation Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Abstract

Assessing the current condition of protective organic coatings on steel structures is an important but challenging task, particularly when it comes to complex structures located in harsh environments. Near-infrared (NIR) spectroscopy is a rapid, low-cost, and nondestructive analytical technique with applications ranging from agriculture, food, and remote sensing to pharmaceuticals. In this study, an objective and reliable NIR-based technique is proposed for the accurate distinction between different coating conditions during their degradation process. In addition, a state-of-the-art deep learning method using a one-dimensional convolutional neural network (1-D CNN) is explored to automatically extract features from the spectrum. The characteristics of the spectrum show a downward trend over the entire wavenumber period, and two major absorption peaks were observed around 5250 and 4400 cm−1. The experimental results indicate that the proposed deep network structure can powerfully extract the complex characteristics inside the spectrum, and the classification accuracy of the training and testing data was 99.84% and 95.23%, respectively, which suggests that NIR spectroscopy coupled with a deep learning algorithm could be used for the rapid and accurate inspection of steel coatings.