Improved Method for Positioning Crane Grab Boom Corner Points using Hough Transform and K-means Clustering
Min Wang 1, Longkun Wan 2 , Chengli Zhao 2,Zhangyan Zhao2*
1 CCCC Second Harbor Engineering Company LTD,Wuhan 430040,China
2 School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063,China
Correspondence:zzy63277@163.com
Abstract: In the process of automatic grabbing of bridge segment beams, in order to ensure that the crane can smoothly calibrate and align the lifting rod with the beam body lifting hole, it is necessary to use image processing technology to locate and detect the corner coordinates of the crane’s grabbing lifting rod. When applying traditional corner detection methods to this scene, there are challenges such as low detection accuracy and unsuitability. This article proposes a new idea for corner positioning, which locates corner coordinates through the intersection of straight lines. This method is divided into three steps: first, use the R and G channels of the RGB color space to construct a grayscale difference map, so that the grayscale histogram of the foreground and background presents a bimodal feature, which is conducive to Otsu’s threshold segmentation. And use the open close operation to denoise the small impurities in the Canny edge detection results; Secondly, this article proposes the optimal adaptive threshold determination method to filter the number of votes in the clustering results, eliminate interfering straight lines, and then improve the clustering centroid calculation method by using weight calculation formulas based on different proportion of votes, replacing the original clustering centroid as the basis for line fitting; Finally, calculate the corner coordinates of the crane’s grab boom based on the straight line fitting results, and compare the recognition accuracy under different lighting conditions. The experimental results show that when there are many interfering edge points in the edge detection result graph, compared to other line detection algorithms, the detection error of our algorithm is smaller and has strong robustness. The calculated corner coordinate accuracy is pixel level. The algorithm in this article has the best detection performance under strong complementary light conditions. The average detection error within 0-2 pixels accounts for 97.1% and a recognition accuracy of 98.6%. The recognition success rate under different lighting conditions is higher than 92.9%. This method is significantly superior to traditional linear detection methods and meets the needs of automatic gripping of the boom. It has certain engineering application value and provides a method basis for solving the algorithm accuracy and robustness problems of port cranes under multiple environmental variables.
Keywords: Crane grabbing boom、Hough transform、K-means clustering、Line fitting、Corner detection