An enhanced centerline extraction algorithm for complex stripes in linear laser scanning measurement

Centerline extraction is a basic and critical step for linear laser scanning measurement. However, in practical measurement, the inconsistent reflectivity of the measured surface and the external noise interference can result in complex variation of the stripe pattern, which will influence the centerline extraction accuracy of the stripe pattern and hence the measurement accuracy of the laser scanner. To solve this problem, an enhanced centerline extraction algorithm for complex laser stripes is proposed. In the preprocessing of the stripe pattern, a region separation strategy is employed to mitigate the effects of complex variations, and multi-region self-adaptive convolution is adopted to enhance the stripe pattern quality. For high-precision extraction of the stripe centerline, different convolution kernels are applied to compute the Hessian matrix for the stripe patterns in different regions to determine the normal direction of the laser line. The second order Taylor expansion is performed along this direction to work with the denoising algorithm to determine the subpixel positions of the center points on the line, and then the whole centerline of the stripe pattern is obtained by piecewise cubic Hermite interpolation methods. Experimental results show that the effectiveness of the proposed algorithm in addressing centerline extraction for complex laser stripe patterns due to stray light interference, reflective stripes, and laser stripe linewidth changes in laser scanning measurement.