为了解决目前节理信息识别方法仅适用于局部岩体图像的问题,采用全景展开图像方法,将采集到的局部洞壁围岩图像进行特征点提取、点云模型重建、矫正拼接处理,获得高分辨率的隧道洞壁围岩全景展开图像。通过图像预处理、小尺寸特征图片的smaAt-Unet神经网络识别、小尺寸图片识别结果的融合拼接,对洞壁围岩全景展开图像的节理信息进行了区域粗略分割识别。采用Zhang-Suen算法和8邻域连通域分析方法,从骨架化与骨架线分离、毛刺剔除、骨架线连接方面进行了算法分析计算,完成了节理信息的细化提取。对体积节理数和节理空间产状信息进行了量化分析,最终建立了一种基于全景展开图像的隧道洞壁围岩节理信息识别方法。工程应用结果表明,洞壁围岩全景展开图像识别后,节理面空间方程的平均拟合误差为0.90,说明该识别方法能够较好地识别全景展开图像中的节理信息。另外,洞壁围岩图像采集具有时间短、易操作、灵活性较高的优点,对现场施工影响较小,该识别方法能够较为快速地完成节理信息识别,给现场施工与动态设计提供参考。
Abstract
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 skeletonization, 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 number and joint occurrence information, we developed the method to identify rock joint information based on panoramic developed images. Application results demonstrate an average fitting error of 0.90 of the spatial equation of jointed plane, indicating successful joint information identification. Moreover, the panoramic developed imaging technique boasts advantages such as rapidity, simplicity, and flexibility, with minimal impact on site construction.
关键词
隧道 /
洞壁围岩 /
全景展开图像 /
smaAt-Unet神经网络 /
节理识别
Key words
tunnel /
surrounding rock /
panoramic developed image /
SmaAt-Unet neural network /
joint recognition
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基金
国家自然科学基金高铁联合基金项目(U1934211);中南大学研究生自主探索创新项目(2021zzts0241);犬木塘水库工程科技创新项目 (W-2022-72)
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