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
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