GMM-based Automatic Defect Recognition Algorithm for Pressure Vessels Defect Detection through ECPT

Abstract

In order to realize the automatic identification of pressure vessel defects, an improved adaptive defect recognition feature extraction algorithm through ECPT (Eddy current pulsed thermography) is proposed. The proposed feature extraction algorithm consists of five elements: thermal image data segmentation, variable interval search, probability density function modeling, data classification, and reconstructed image acquisition. The combination of data block selection and variable interval search can reduce the double counting. And the KG-EM (Kmeans-GMM-EM) algorithm is proposed to obtain the Gaussian mixture model corresponding to the classification, and thus the corresponding probability is obtained to classify the TTRs (Transient Thermal Response). The reconstructed thermal image is obtained by the classified TTRs. This method can extract the main information of the image accurately and efficiently. Experimental results are provided to demonstrate their effectiveness.