Adaptive rapid defect identification in ECPT based on K-means and automatic segmentation algorithm

• Published in: Journal of ambient intelligence and humanized computing Vol. 14; no. 11; pp. 1 - 18
• Main Authors: Huang, Xuegang, Yin, Chun, Dadras, Sara, Cheng, Yuhua, Bai, Libing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial Intelligence; Cameras; Cluster analysis; Clustering; Computational Intelligence; Decision analysis; Defects; Eddy currents; Efficiency; Engineering; Feature extraction; Fourier transforms; Image segmentation; Infrared imagery; Mathematical analysis; Original Research; Robotics and Automation; Thermal imaging; Thermography; User Interfaces and Human Computer Interaction; Between-class distance; Eddy current pulsed thermography; Inner-class-distance; Transient thermal response; K-means clustering; Pearson correlation
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-017-0671-5

To enhance the detection efficiency in eddy current pulsed thermography, an adaptive feature extraction algorithm for defect identification is developed in this paper. The proposed algorithm involves four stages, namely, the thermal image segmentation, the variable interval search, the distance correlation clustering analysis and the between-class distance decision making. The transient thermal responses ( TTRs ) with similar characteristics are collected into one data block. The thermal image segmentation and variable interval search can help reduce the repetitive calculation in defect identification by choosing local optimums in each data block. The global optimum that has the largest sum of the between-class distance, is derived by first classifying the local optimums and then calculating the correlation distance of the thermal responses with the center points of each class. Finally, the TTRs with the largest between-class distance are regarded as the typical ones which can be used to identify the discriminative defect features of infrared image sequence. Finally, the comparison experiments are carried out to demonstrate the effectiveness and advantages of the proposed approach.