University of Electronic Science and Technology of China

Chun Yin

Yuhua Cheng

Kai Chen

Xuegang Huang

Ting Xue

Haonan Zhang

Gen Qiu

Yi Bu Li

Jie Zhang

Libing Bai

Xiao Yang

Xutong Tan

Zeqi Wang

Anhua Shi

Zhibo Li

Aolin Yang

Jianhao Luo

Yonggang Wang

Junjie Liu

Shuai Shi

Xue Chen

Yinze Wang

A method for high-precision true color three dimensional reconstruction of mechanical component. Firstly performs image acquisition: the left and right high-resolution grayscale cameras are fixed at same height and spaced at certain distance, an optical transmitter fixed between the two grayscale cameras, and low-resolution color camera fixed above optical transmitter, thus images of measured high-precision mechanical component are shot. Then performs image processing: all images are transmitted to a computer, which uses image processing to record surface information of measured high-precision mechanical component in the point cloud by high-precision true color three-dimensional reconstruction, which reflects color texture information of the surface, so as to realize the non-contact high-precision true color three dimensional reconstruction of high-precision mechanical component. The method uses binocular high-precision grayscale cameras instead of binocular color cameras, which broadens the range of capture wavelengths, retains richer texture details of high-precision mechanical component and improves accuracy of measurement.

Method for high-precision true color three-dimensional reconstruction of a mechanical component

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 &#x3b5;&#xd7;d, finally, based on the thermal diffusion points, the accurate identification of quantitative size of defects are completed.

Method for quantitatively identifying the defects of large-size composite material based on infrared image sequence

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.

Method for separating out a defect image from a thermogram sequence based on weighted naive bayesian classifier and dynamic multi-objective optimization

The present invention provides a method for separating out a defect image from a thermogram sequence based on feature extraction and multi-objective optimization, we find that different kinds of TTRs have big differences in some physical quantities, such as the energy, temperature change rate during endothermic process, temperature change rate during endothermic process, average temperature, maximum temperature. The present invention extract these features (physical quantities) and cluster the selected TTRs into L clusters based on their feature vectors, which deeply digs the physical meanings contained in each TTR, makes the clustering more rational, and improves the accuracy of defect separation. Meanwhile, the present invention creates a multi-objective function to select a RTTR for each cluster based on multi-objective optimization. The multi-objective function does not only fully consider the similarities between the RTTR and other TTRs in the same cluster, but also considers the dissimilarities between the RTTR and the TTRs in other clusters, the RTTR is more representative, which guarantees the accuracy of describing the defect outline.

Method for separating out a defect image from a thermogram sequence based on feature extraction and multi-objective optimization

The present invention provides a method for simulating magnetic flux leakage based on loop current, the magnetic distribution of a single loop current is obtained by solid angle, then the magnetic field distribution of a semi-infinite solenoid is obtained based on the assumption of arrangement of loop currents in the magnetized specimen, and its equation is given; and the JA hysteresis model is introduced to obtain the distribution of quasi-static magnetic flux leakage field, on the basis of analyzing the relation between the distribution of a loop current on the surface of the defect and the excitation magnetic field, the distribution of quasi-static magnetic flux leakage field is obtained based on the semi-infinite solenoid.

Method for simulating magnetic flux leakage based on loop current

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Chengdu, China

Chun Yin