【摘要】 对掌子面图像的裂隙识别和特征提取进行研究，首先根据隧道中光照不足和光线不均匀的特点，对掌子面图像集进行包含多种光照处理措施在内的数据增强；通过Unet网络识别掌子面轮廓，其平均交并比和平均相似度为91%和93%；利用形态学操作使掌子面轮廓边缘平滑，消除噪点。然后利用拆分-拼接策略处理高分辨率掌子面图像，通过DeepCrack网络模型迁移学习识别岩体裂隙，其平均交并比和平均相似度为61%和75%。利用Zhang-Suen算法和8邻域标记算法进一步对识别结果进行细化、骨架化和连通域分析。最后，通过控制点标记和腐蚀标记法计算每条裂隙的像素级长度和倾角。更多还原

【Abstract】 In this paper, the fracture identification and feature extraction of tunnel face images are studied. Firstly,the data enhancement including various illumination processing measures is carried out on the tunnel face image set based on the characteristics of insufficient illumination and uneven light in the tunnel. Though recognizing the contour of the tunnel face by the Unet network, the average value of intersection over union and the average similarity are 91% and 93%. The morphological operation is used to make the edge of the tunnel face contour smooth and eliminate noise points. Then, the high-resolution tunnel face images are processed by splitting-splicing strategy, and the rock mass fractures are identified by DeepCrack network model transfer learning. The average value of intersection over union and average similarity are 61% and 75%. Further, the identification results are refined, skeletonized and analyzed in connected domains by using the Zhang-Suen algorithm and the 8 neighborhoods labeling algorithm. Finally, the pixel-level length and dip angle of each fracture is calculated by the control point marking and corrosion marking methods.更多还原