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

To resolve the disparity between accuracy and speed in the current surface flatness detection of medium and thick steel plates, which predominantly relies on the single-line laser vision method, we propose a high-efficiency flatness detection method for medium-thick steel plates based on multiline parallel laser vision. On the basis of building a flatness detection system, we present the adaptive cropping algorithm and improved variable threshold Otsu thresholding segmentation algorithm (IVT-Otsu) for image preprocessing. Then, the centerline of subpixel-level laser stripes is accurately extracted using a weighted quadratic grayscale center of gravity method. Meanwhile, a pruning algorithm-based multiline laser stripe center extraction method is proposed to address the phenomenon of spotting and branching in the center of multiline parallel laser, to efficiently obtain the surface depth information and flatness index of medium-thick steel plate. Results of the experiment indicate that the proposed method better fulfills the accuracy and efficiency requirements for online inspection when extracting the laser centers and calculating the flatness.