A Weld Feature Extraction Method Based on Lightweight Instance Segmentation and Point Cloud Features

PurposeNon-standard structural components present substantial challenges for robotic welding path planning. This study aims to propose an efficient method for weld feature extraction that combines lightweight instance segmentation with point cloud analysis to overcome these challenges.Design/methodology/approachThe method uses a You Only Look Once-Fast (YOLO-Fast) model to segment workpieces and map 2D image pixels to 3D point cloud data, enabling precise extraction of regions of interest. A random sample consensus-oriented bounding box algorithm is then employed to localize weld feature points accurately.FindingsYOLO-Fast reduces model parameters by 39.08%, lowers computational cost by 29.15% and increases inference speed by 17.17%, while maintaining high segmentation accuracy. Experiments on tower base structures achieve a root mean square error (RMSE) below 2.98 mm, improving extraction accuracy by at least 15.4%. The RMSE for inner and outer weld features reaches 1.96 mm, demonstrating the method's robustness and practical applicability.Originality/valueThis study introduces a novel approach for efficient and precise weld feature extraction in complex, nonstandard structural components, providing a foundation for enhanced robotic welding path planning in challenging industrial scenarios.