XLPE cable joint defects measurement method based on point cloud remapping

Abstract

For the problems of low detection accuracy caused by weak texture and strong reflection on the surface of Cross-linked polyethylene (XLPE) high-voltage cable joint, this paper proposed a joint point cloud remapping and image segmentation method for the cable joint defects measurement, which improved the defects measurement accuracy from the correlation of the disordered point cloud and the salience of defects. Firstly, the discrete noise in the joint point cloud is removed using a modified radius filtering algorithm. Secondly, for the problems of disorderliness and weak association of the cable joint point cloud, we utilized Rodrigues' rotation formula (RRF) to correct the position of the XLPE cable joint point cloud. Then, for the demand of improving feature expression of non-significant defects, a point cloud remapping method is proposed, which remapping insignificant defects point cloud into saliency image for more effective detection. Lastly, an image segmentation method based on 8-neighborhood growth is applied for explicit defects detection, and the detected defects are quantified by the proposed measurement approach. Extensive experiments are conducted on the collected cable joints' point cloud and simulated cable joints' point cloud. With the help of the proposed method, we can achieve the best effects in DE, FR quantitative comparison, and the measurement inaccuracies are less than 4.5%. Experimental results demonstrate that the proposed method enjoys state-of-the-art performance in cable joint defects measurement.