Research on Main Insulation Defect Detection in High-Voltage Cable Joints Using 3D Point Cloud Technology

To address the issues of low accuracy and efficiency in detecting defects on the main insulation surface of high-voltage cable joints, this paper proposes a defect detection method based on 3D point cloud technology. An improved filtering algorithm combining adaptive statistical filtering and bilateral filtering is applied to denoise the acquired point cloud data, resulting in accurate and reliable point cloud data. Furthermore, a defect extraction method based on the RANSAC algorithm is introduced, and experimental studies are conducted on the extraction and quantification of defects on the main insulation surface. The results demonstrate that 3D point cloud reconstruction can effectively identify depression and protrusion defects on the main insulation surface of high-voltage cables, with a quantification accuracy of less than 5%, meeting the requirements for field applications.