数字钻孔图像岩体结构面自动化识别方法

数字钻孔摄像技术能准确获取钻孔中岩体结构面特征信息，针对现有数字钻孔图像分析人力需求量大、主观性强、计算量大的不足，研究提出方案实现数字钻孔摄像技术采集的钻孔内壁图像自动识别。首先，用二维伽马函数光照自适应矫算法对图像进行光照均匀处理，利用经过预训练的DexiNed网络对矫正后图像边缘进行特征提取；其次，提出Epremoval方法处理边缘点噪声提取感兴趣区域；最后，根据正弦曲线泰勒展开式对图像中的表征数据进行多项式拟合。通过对得到曲线进行计算、空间变换和数理变换得到岩体结构面参数。以某隧道工程的数字钻孔图像为例，研究提出的算法结果优于人工辅助判读结果。

Automatic identification of rock structure surface based on digital borehole images

Digital borehole camera technology can accurately acquire information regarding the structural surface characteristics of rock within a borehole. To address the shortcomings such as labor-intensity, subjectivity, and computational intensity associated with existing digital borehole image analysis, this paper introduces a new analysis scheme to automate the recognition of borehole interior images captured by digital borehole camera technology. The proposed scheme begins by uniformly illuminating images using a two-dimensional gamma function light-adaptive correction algorithm. Next, edge features are extracted using a pre-trained DexiNed network. To tackle edge point noise and extract the region of interest, the Epremoval method is employed. Finally, the method performs polynomial fitting on the characterization data in the image utilizing the Taylor expansion of the sine curve. The parameters of the rock structure surface are obtained by calculation, spatial transformation and mathematical transformation of the obtained curves. The algorithm is applied to the digital borehole image of a tunnel project as an illustrative example. The obtained results are compared with the results of manual assisted interpretation, revealing superior recognition capabilities of the proposed method.