-
公开(公告)号：US11587250B2
公开(公告)日：2023-02-21
申请号：US17401760
申请日：2021-08-13
Inventor： Yuhua Cheng , Chun Yin , Xiao Yang , Kai Chen , Xuegang Huang , Gen Qiu , Yinze Wang
Abstract: The present invention provides a method for quantitatively identifying the defects of large-size composite material based on infrared image sequence, firstly obtaining the overlap area of an infrared splicing image, and dividing the infrared splicing image into three parts according to overlap area: overlap area, reference image area and registration image area, then extracting the defect areas from the infrared splicing image to obtain P defect areas, then obtaining the conversion coordinates of pixels of defect areas according to the three parts of the infrared splicing image, and further obtaining the transient thermal response curves of centroid coordinate and edge point coordinates, finding out the thermal diffusion points from the edge points of defect areas according to a created weight sequence and dynamic distance threshold εttr×dp_max, finally, based on the thermal diffusion points, the accurate identification of quantitative size of defects are completed.
-
公开(公告)号：US11036978B2
公开(公告)日：2021-06-15
申请号：US16370202
申请日：2019-03-29
Inventor： Chun Yin , Yuhua Cheng , Ting Xue , Xuegang Huang , Haonan Zhang , Kai Chen , Anhua Shi
Abstract: The present invention provides a method for separating out a defect image from a thermogram sequence based on weighted naive Bayesian classifier and dynamic multi-objective optimization, we find that different kinds of TTRs have big differences in some physical quantities. The present invention extracts these features (physical quantities) and classifies the selected TTRs into K categories based on their feature vectors through a weighted naive Bayesian classifier, which deeply digs the physical meanings contained in each TTR, makes the classification of TTRs more rational, and improves the accuracy of defect image's separation. Meanwhile, the multi-objective function does not only fully consider the similarities between the RTTR and other TTRs in the same category, but also considers the dissimilarities between the RTTR and the TTRs in other categories, thus the RTTR selected is more representative, which guarantees the accuracy of describing the defect outline. And the initial TTR population corresponding to the approximate solution for multi-objective optimization is chosen according to the previous TTR populations, which makes the multi-objective optimization dynamic and reduces its time consumption.
-
Search Results
2
records
(took 0.018 seconds)
