Rapid Recognition of Rock Mass Fractures in Tunnel Faces
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摘要: 隧道掌子面上含有许多地质信息，若能充分提取和分析，将有助于对隧道工程地质状态作出评价，用于指导隧道设计和施工. 以隧道掌子面数码图像为基础，对掌子面上岩体裂隙检测、提取、分组算法进行研究. 首先，基于数字图像处理技术对掌子面岩体裂隙目标分割算法进行分析，根据分割结果，通过图像细化和边界线拟和、分离、合并、过滤，连接不连续边界，过滤短边界，形成较完整的岩体边界线识别结果；然后，计算岩体边界线视倾角，将视倾角相近的边界线合并为一组，实现对裂隙边界线的自动分组；最后，将本方法应用于实际掌子面岩体图像测试其有效性. 测试结果表明：该方法基本实现了对掌子面上岩体裂隙的自动提取和分组；对具有明显裂隙的掌子面岩体，本算法能较完整的提取出岩体裂隙，错误提取率不超过10%，并实现了自动分组，自动分组错误率不超过5%，提高了掌子面岩体分析的自动化程度，可用于地质素描，并为掌子面围岩分级提供参考依据.Abstract: Tunnel faces contain much geological information, which, if fully extracted and analyzed, will help to evaluate the geological state of tunnel engineering and guide tunnel design and construction. In this paper, rock mass crack detection, extraction and grouping algorithms of the tunnel face are studied on the basis of the tunnel face image. First, the image segmentation algorithm for face rock mass cracks is analyzed using digital image processing technology. According to the segmentation results, the discontinuous boundaries are connected and the short boundaries are filtered through image thinning and boundary fitting, separation, merging and filtering to form a relatively complete recognition result of rock mass boundary lines. Then, the apparent dip angle of rock mass boundary line is calculated, and the boundary lines with similar apparent dips are merged into a group to achieve automatic grouping. This method is applied to real tunnel face images to test its effectiveness. Results show that the proposed method can basically realize automatic extraction and grouping of the rock mass cracks in the funnel face. For rock masses with obvious cracks, the algorithm can extract the cracks with an error rate of more than 10% and implement automatic grouping with an error rate of no and 5%. This method improves automation degree of rock mass analysis of the tunnel face, and can be used for geological sketching map, providing references for the surrounding rock classification of tunnel faces.
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Key words:
- tunnel face /
- image processing /
- rock mass crack recognition
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图 3 单像素宽裂隙边界线处理示意
Figure 3. Diagram of single-pixel-width fracture boundary line processing
图 7 两近邻多段线相似性分析示意
Figure 7. Schematic diagram of similarity analysis of neighboring polylines
图 9 掌子面图像及岩体裂隙边界提取与分组结果
Figure 9. Image of the face and the boundary of the rock mass are extracted and grouped.
表 1 岩体裂隙自动提取及分组结果与人工修正结果对比
Table 1. Comparison of automatic extraction results and artificial correction results of rock mass fractures
图像序号 自动提取参数设置及提取结果 人工修正情况 自动分组及处理结果 dT /
PixelsαT/（º） DT/
PixelsβT/（º） LT/
Pixels总长度Lt/
Pixels删除总长度Ld/Pixels 删除率Rd/% 添加总长度La/Pixels 添加率Ra/% 错误分组边界线条数 Ne/条 错误分组长度Le/Pixels 错误分组
率Re/%1 2 155 400 155 39.7 3978.0 92.8 2.3 217.6 5.5 1 39.7 1 2 2 155 400 155 47.1 4736.5 107.8 2.2 11.0 2.5 0 0 0 3 2 155 400 155 35.3 3583.0 195.4 5.5 200.0 5.6 0 0 0 
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