Gradient feature-based method for Defect Detection of Carbon Fiber Reinforced Polymer Materials

The structural and material aging of the energy and transportation infrastructure requires the development of faster, better, and more efficient non-destructive evaluation (NDE) techniques to assess remaining life and structural health for prognostics and structural health management. Composite materials such as carbon fiber reinforced polymer (CFRP) have beneficial properties such as corrosion resistance, durability, and lightweight, which reduce maintenance requirements and extend service life. Their an-isotropic dielectric and mechanical properties make it challenging for NDE techniques to detect and locate material defects. A miniaturized capacitive imaging system was developed to detect multiple types of defects in CFRP materials. However, algorithms to convert the raw imaging data into defect detection, classification, sizing, and location is not currently available. This paper presents a defect localization algorithm using a gradient response feature-based method to reduce the noise in the imaging data. The algorithm calculates the co-occurrence matrix of the image. From this matrix, the local features such as contrast, homogeneity, energy, and correlation are extracted. A combination of these features is selected to define a defect area. The features extracted from the image processing are classified using a support vector machine (SVM) algorithm. The location of the defects identified through the algorithm is compared with the ground truth to achieve a probability of detection of 82%.