A method for classifying stains and defects on mobile phone cover glass based on hyperspectral remote sensing

Hyperspectral imaging is an efficient way to overcome the limitations of detecting different object with similar visible light texture. The study aims to expand the feasibility of hyperspectral imaging to classifying the stains and defects on mobile phone cover glass. Firstly, we extracted eight optimal spectral features by decision tree method, including 526 nm, 567 nm, 582 nm, 629 nm, 689 nm, 711 nm, 789 nm, and 888 nm. Our classification used the Random Forest modeling method (RF). Experimental results showed that, based on optimal spectral features, the precision of RF model outperformed for classifying stains and defects (Precision > 0.9). Overall, this study contributes a reliable and convenient tool for classification of stains and defects on mobile phone cover glass, offering scientific insights to support quality control inspection for mobile phone cover glass.