Drill image dataset for training part II.

Structural planes decrease the strength and stability of rock masses, severely affecting their mechanical properties and deformation and failure characteristics. Therefore, investigation and analysis of structural planes are crucial tasks in mining rock mechanics. The drilling camera obtains image information of deep structural planes of rock masses through high-definition camera methods, providing important data sources for the analysis of deep structural planes of rock masses. This paper addresses the problems of high workload, low efficiency, high subjectivity, and poor accuracy brought about by manual processing based on current borehole image analysis and conducts an intelligent segmentation study of borehole image structural planes based on the U2-Net network. By collecting data from 20 different borehole images in different lithological regions, a dataset consisting of 1,013 borehole images with structural plane type, lithology, and color was established. Data augmentation methods such as image flipping, color jittering, blurring, and mixup were applied to expand the dataset to 12,421 images, meeting the requirements for deep network training data. Based on the PyTorch deep learning framework, the initial U2-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. Overall, the model has high accuracy for segmenting structural planes and very low mean absolute error, indicating good segmentation accuracy and certain generalization of the network. The research method in this paper can serve as a reference for the study of intelligent identification of structural planes in borehole images.

结构面（structural planes）会降低岩体的强度与稳定性，严重影响其力学特性以及变形破坏特征。因此，结构面的调查与分析是采矿岩石力学中的关键任务。钻孔摄像设备通过高清摄像方式获取岩体深部结构面的图像信息，为岩体深部结构面的分析提供重要数据源。针对当前钻孔图像分析依赖人工处理所带来的工作量大、效率低下、主观性强以及准确率低等问题，本文基于U2-Net网络开展了钻孔图像结构面智能分割研究。通过采集不同岩性区域的20幅钻孔图像数据，构建了包含1013张标注有结构面类型、岩性与色彩信息的钻孔图像数据集。采用图像翻转、色彩抖动、模糊处理以及mixup等数据增强方法，将数据集扩充至12421张，满足深度网络训练的数据需求。基于PyTorch深度学习框架，设置U2-Net网络的初始权重，将学习率设为0.001，训练批次大小设为4，并在训练过程中采用Adam优化器自适应调整学习率。最终得到一款专用的结构面分割网络模型，当置信阈值设为0.7时，该模型的最大F测度（F-measure）值可达0.749；在召回率大于0.5的区间内，准确率最高可达0.85。整体而言，该模型在结构面分割任务中具备较高的准确率，且平均绝对误差极低，表明其分割精度优异，网络具备一定的泛化能力。本文的研究方法可为钻孔图像结构面智能识别相关研究提供参考借鉴。