A laser vision sensing method for seam tracking based on an improved TLD algorithm

In order to solve the problem of low positioning accuracy of the weld seam center based on line laser vision sensing, a seam tracking method based on an improved tracking-learning-detection (TLD) algorithm was adopted. The weld images were acquired in real time during the weld seam tracking. The TLD algorithm combining the tracker (tracking) and the detector (detection) was adopted to track weld feature points in real time and the online learning mechanism (learning) was adopted to update the classifier parameters, so as to improve the accuracy of seam tracking. On this basis, the region of interest (ROI) was intercepted from the laser stripe images, which greatly reduced the detector's search area. The effective feature points of the tracker were selected to improve the efficiency of the algorithm according to the characteristics of the light intensity distribution of the laser stripe in combination with the rectifying direction. The V-shaped weld and the lapped weld of the stainless steel plate were tracked. The results indicate that the location of the seam center can be achieved by tracking and detecting and the fusion weld tracking method can accurately extract weld feature points. The mean absolute tracking errors of both weld seams were 0.062mm and 0.052mm. This method provides the basis for improving the accuracy of weld seam tracking.