Enhancing Ship Coating Quality Detection via Machine Learning-Optimized Visible Near-Infrared Spectroscopy

This study proposes a portable Visible/Near-Infrared (Vis/NIR) spectroscopy-based approach to detect and evaluate the quality of ship coatings.Vis/NIR spectroscopy offers an accurate, non-destructive method for identifying coating conditions through spectral data acquisition, combined with machine learning analysis to improve detection performance.In this study, using a device with a wavelength of 410-940 nm, spectral transformations such as scatter correction, baseline correction, smoothing, and derivative were applied to improve data quality, followed by feature selection using PCA and IFS.SVM, Random Forest (RF), and LDA classification algorithms were then used to model spectral data.The coating quality consists of four classes, with 40 samples for each.The initial results of modeling without treatment were improved with an average accuracy of 83.90%.Then, applying the combination of Nippy and IFS significantly increases average accuracy results by 96.86%.Incorporating spectral transformation and feature selection methods can optimally utilize spectral information and improve the model's overall performance with an increase in accuracy of 12.96%.