Automatic detection of Kikuchi bands based on Radon transform and PPHT

The information of crystal structure and orientation can be provided by analysing the EBSD (electron backscatter diffraction) patterns which are obtained with the EBSD devices. The reliability and accuracy of the information relies on the location of bands and intersections of the EBSD patterns. In this study, a method is proposed to automatically obtain the locations and intersections of the EBSD patterns, that is, Kikuchi bands. The proposed method uses Radon transform and progressive probabilistic Hough transform to detect straight lines and line segments of the Kikuchi band edges, respectively. Then, Kikuchi bands can be presented by fitting the hyperbolas with the endpoints of line segments. The results can numerically describe the information of Kikuchi bands. Experimental results show that the method is robust and can detect more accurate Kikuchi bands and intersections. Lay description In this paper, a novel method is proposed to detect the electron backscatter diffraction patterns. Electron backscatter diffraction patterns are a class images consisting of multiple parallel lines of light and dark pairs. The bands on the image can reflect the information of crystal structure and orientation. Most existing methods are complex to implement and computationally intensive in detecting edges and intersections of bands. Therefore, we designed a fast and easy‐to‐implement detection method with relatively good accuracy to overcome the drawbacks of existing methods. Our method is based on straight line detection and line segment detection. After matching the straight line detection results and the line segment detection results, the edges are obtained by fitting the line segment endpoints using a hyperbola, and the intersections are obtained by using centerline positioning. Experiments have shown that our method has good accuracy and can detect less perfect patterns . In addition, our method is easy to implement and and is valuable for computationally constrained cases.