Mobile Hyperspectral Imaging for Material Surface Damage Detection
Sameer Aryal, Zhiqiang Chen, Shimin Tang
Abstract
Many machine vision–based inspection methods aim to replace human-based inspection with an automated or highly efficient procedure. However, these machine-vision systems have not been endorsed entirely by civil engineers for deployment in practice, partially due to their poor performance in detecting damage amid other complex objects on material surfaces. This work developed a mobile hyperspectral imaging system which captures hundreds of spectral reflectance values in a pixel in the visible and near-infrared (VNIR) portion of the electromagnetic spectrum. To prove its potential in discriminating complex objects, a machine learning methodology was developed with classification models that are characterized by four different feature extraction processes. Experimental validation showed that hyperspectral pixels, when used conjunctly with dimensionality reduction, possess outstanding potential for recognizing eight different surface objects (e.g., with an F1 score of 0.962 for crack detection), and outperform gray-valued images with a much higher spatial resolution. The authors envision the advent of computational hyperspectral imaging for automating damage inspection for structural materials, especially when dealing with complex scenes found in built objects in service.