Weld Seam ROI Detection and Segmentation Method Based on Active-Passive Vision Fusion

Rapid detection and precise segmentation of the weld seam region of interest (ROI) remain a core challenge in robotic intelligent grinding. To address this issue, this paper proposes a method for weld seam ROI detection and segmentation based on the fusion of active and passive vision. The proposed approach primarily consists of two stages: weld seam image instance segmentation and weld seam ROI point cloud segmentation. In the image segmentation stage, an enhanced segmentation network is constructed by integrating a convolutional attention module into YOLOv8n-seg, which effectively improves the localization accuracy and mask extraction quality of the weld seam region. In the point cloud segmentation stage, the 3D point cloud is first mapped onto a 2D pixel plane to achieve spatial alignment. Subsequently, a coarse screening of the projected point cloud is performed based on the bounding boxes output from the instance segmentation, eliminating a large amount of redundant data. Furthermore, a grayscale matrix is constructed based on the segmentation masks, enabling precise extraction of the weld seam ROI point cloud through point-wise discrimination. Experimental results demonstrate that the proposed method achieves high-quality segmentation of the weld seam region, providing a reliable foundation for robotic automated grinding.