Signal Enhancement in Defect Detection of CFRP Material Using a Combination of Difference of Gaussian Convolutions and Sparse Principal Component Thermography

Owing to its advantages of low-cost and fast detection, pulsed thermography has become a promising technique to detect subsurface defects in materials of carbon fiber reinforced polymer (CFRP). Since defect signals in the detected results always suffer from low contrast due to the instability of detecting environment, feature extraction methods are required to enhance the visualization of defects. However, many state-of-the-art feature extraction methods have difficulties in overcoming the interference from noise and background, so that their effects are limited in highlighting the defects. To solve this problem, a novel methodology of combining signal filtering with feature extraction is proposed in this paper. In this approach, thermal images are first smoothed by a difference of Gaussian convolutional (DoGC) filters, which is designed to eliminate noise and uneven background based on their frequencies. Furthermore, the method of sparse principal component thermography (SPCT) is adopted to extract the features of defects. Two experiments on sample laminates have suggested that, DoGC-SPCT is superior to other feature extraction methods in the following aspects. Firstly, the DoGC filter can effectively eliminate most of the interference, thus facilitating defect identification during the process of feature extraction. Secondly, the computational outcomes show that DoGC-SPCT leads to higher values in the index of signal to noise ratios for the defects. Finally, DoGC-SPCT leads to higher interpretability, which has smoother background in the obtained results.