Detection of crack defects in carbon fiber composites using passive infrared thermography

Abstract

Aiming at the problem of low detection accuracy in detection of damage location, This article proposes a method to reduce the local jitter in passive infrared thermal imaging by using wavelet hierarchic hard threshold denoising based on template convolution, and thresholds are defined using an unsupervised Kmeans classification algorithm, the traditional SAD template convolution matching algorithm and the Sobel operator edge detection are improved. The denoizing algorithm achieves improved the denoising strength, the smoothness, and effectively extracts the crack defect features. To increase the effectiveness of nondestructive examination, the rapid detection method of temperature data matching is utilized before the infrared detection experiment, and the damage position was marked on the normal sample to allow for intuitive assessment of the damage location. The results show that by matching temperature data, the damage degree of the specimen can be quickly determined, and that the damage features can be accurately extracted using improved image processing algorithm.