Fast and accurate centerline extraction algorithm for a laser stripe applied for shoe outsole inspection

Abstract

Line laser 3D reconstruction technology is widely used in industrial applications. As a key step of this technology, line laser midline extraction directly affects the accuracy of the 3D reconstructed model. In reconstructing the shoe outsole, the traditional algorithm based on the threshold method to determine the laser position may result in a large amount of information loss and miscellaneous point misjudgment owing to the irregularity of the shoe outsole surface, which critically affects the laser imaging quality. To address this problem, an algorithm based on the QQ plot inspection of the laser has been proposed. The QQ plot is a scatter plot, the abscissa is usually the quantile of the standard normal distribution, and the ordinate is the quantile of the data to be tested. If the points on the scatter plot tend to be straight lines, the data to be tested is in a normal distribution. Based on this property, the proposed algorithm aims to check whether the pixels of the image column tend to be normally distributed, rather than using traditional thresholding methods to locate the laser. The objective is to examine whether the image column pixel distribution is normal, instead of using the traditional threshold method to locate the laser. However, the calculation speed of this method is extremely low. To enhance the efficiency of testing the normality of the QQ plot, a quantile-repetition (Q-R) test method is proposed. In this approach, the degree of repetition of quantiles and the position of Q-R values are used to replace the QQ plot based evaluation of the points being on a straight line, and the exact center position is determined by the GGM. The experimental results show that the proposed algorithm can extract more effective points and fewer invalid points of the laser compared to those obtained using the traditional approach, in a rapid, stable, and accurate manner.

High-efficiency flatness detection method for medium-thick steel plates based on multiline parallel laser vision

“…The proposed pruning algorithm has good effects in solving the problem of branching in the extracted laser stripe centers, effectively removing the influence of multiline laser center spots and greatly improving the extraction accuracy. Algorithm GCOG Steger's algorithm Proposed method Rao's algorithm 32 Sheng's algorithm 35 Depth value (mm) faster than Rao's algorithm and 2.2 times faster than Sheng's algorithm. The least amount of time is required to obtain the depth value of the medium-thick steel plates, and the fastest extraction speed can meet the real-time requirements of the industrial measurement system.…”

“…The experiment on laser stripe center extraction will be conducted from the perspectives of accuracy, applicability, and real-time performance. Choose the traditional GCOG, the Steger’s algorithm, Rao’s 32 algorithm, and Sheng’s 35 algorithm as comparative algorithms to evaluate the effectiveness of the proposed method in this paper.…”