Automatic seeded region growing for thermography debonding detection of CFRP

Abstract

The carbon fiber reinforced polymer (CFRP) has been widely used in aerospace, automobile and sports industries. In laminated composite materials, cyclic stresses and impact will cause internal defects such as delamination and debonding. In order to guarantee internal quality and safety, optical pulsed thermography (OPT) nondestructive testing has been used to detect the internal defects. However, current OPT methods cannot efficiently tackle the influence from uneven illumination, and the resolution enhancement of the defects detection remains as a critical challenge. In this paper, a hybrid of thermographic signal reconstruction (TSR) and automatic seeded region growing (ASRG) algorithm is proposed to deal with the thermography processing of CFRP. The proposed method has the capability to significantly minimize uneven illumination and enhance the detection rate. In addition, it has the capacity to automate segmentation of defects. It also overcomes the crux issues of seeded region growing (SRG) which can automatically select the growth of image, seed points and thresholds. The probability of detection (POD) has been derived to measure the detection results and this is coupled with comparison studies to verify the efficacy of the proposed method.