A Recognition Method for Surrounding Rock Joints of Tunnel Based on Panoramic Developed Images
Current methods of joint information recognition are only applicable to local rock images.To address this limitation,we employed the panoramic developed imaging technique to extract image features,reconstruct point-cloud model,and correct and stitch the collected local rock images,thereby obtaining high-resolution panoramic image of the tunnel's surrounding rock mass.Through image pre-processing and recognition of small-size feature images by SmAt-Unet neural network,followed by fusion of the recognition results,we roughly recognized the joint occurrences in the panoramic image region.Subsequently,we extract the refined joint information via skeletoniza-tion,skeleton line separation,burr removal,and skeleton line connection using the Zhang-Suen algorithm and the 8-neighborhood connected domain analysis method.Ultimately,through quantified analysis of volumetric joint num-ber and joint occurrence information,we developed the method to identify rock joint information based on panoram-ic developed images.Application results demonstrate an average fitting error of 0.90 of the spatial equation of joint-ed plane,indicating successful joint information identification.Moreover,the panoramic developed imaging tech-nique boasts advantages such as rapidity,simplicity,and flexibility,with minimal impact on site construction.
tunnelsurrounding rockpanoramic developed imageSmaAt-Unet neural networkjoint recognition
万方数据
维普
