Real-time tracking welding with the assistance of structured light vision enhances the intelligence of robotic welding, which significantly shortens teaching time and guarantees accuracy for user-customized product welding. However, the robustness of most image processing algorithms is deficient during welding practice, and the security regime for tracking welding is not considered in most trajectory recognition and control algorithms. For these two problems, an adaptive feature extraction algorithm was proposed, which can accurately extract the seam center from the continuous, discontinuous or fluctuating laser stripes identified and located by the CNN model, while the prior model can quickly remove a large amount of noise and interference except the stripes, greatly improving the extraction accuracy and processing speed of the algorithm. Additionally, the embedded Pauta criterion was used to segmentally process the center point data stream and to cyclically eliminate outliers and further ensure the accuracy of the welding reference point. Experimental results showed that under the guarantee of the above-mentioned seam center point extraction and correction algorithms, the tracking average error was 0.1 mm, and even if abnormal trajectory points existed, they did not cause welding torch shaking, system interruption or other accidents
