基于YOLOv11的焊缝识别跟踪算法研究OA

To address the problems with weld seam detection accuracy and real-time tracking ability in the field of automated welding,a weld seam identification and tracking method based on YOLOv11 is proposed.The method analyzes the visual features of weld seams,constructs a dataset suitable for weld seam detection,and integrates the laser stripe central line extraction algorithm with the YOLOv11 model,thereby achieving high-precision detection and real-time tracking of weld seams during the welding process.The experimental results show that,at a resolution of 512×512,the proposed method achieves an identification accuracy of 99.51%,with a maximum error of 1.05 pixels in weld seam feature point recogni-tion,an inference speed of 54 ms per image,and an average location error of less than 2.2 mm.These re-sults demonstrate that the proposed method can effectively meet the strict demands of weld seam quality monitoring in intelligent manufacturing production lines.