Rock quality designation (RQD) plays a crucial role in the design and analysis of rock engineering. The traditional method of measuring RQD relies on manual logging by geologists, which is often labor‐intensive and time‐consuming. Thus, this study presents an autonomous framework for expeditious RQD estimation based on two‐dimensional corebox photographs. The scale‐invariant feature transform (SIFT) algorithm is employed for rapid image calibration. A K‐Net‐based model with dynamic semantic kernels, conditional on their actual activations, is proposed for rock core segmentation. It surpasses other prevalent models with a mean intersection over union of 95.43%. The automatic RQD estimation error of our proposed framework is only 1.46% compared to manual logging results, demonstrating its exceptional reliability and effectiveness. The robustness of the framework is then validated on an additional test set, proving its potential for widespread adoption in geotechnical engineering practice.

中文翻译：

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基于 K-Net 的深度学习框架，用于自动岩石质量鉴定估计


岩石质量鉴定 （RQD） 在岩石工程的设计和分析中起着至关重要的作用。测量 RQD 的传统方法依赖于地质学家的手动记录，这通常是劳动密集型和耗时的。因此，本研究提出了一个基于二维核心盒照片的快速 RQD 估计的自主框架。采用尺度不变特征变换 （SIFT） 算法进行快速图像标定。提出了一种基于 K-Net 的模型，该模型具有动态语义内核，以它们的实际激活为条件，用于岩芯分割。它超过了其他流行的模型，平均交集与并集之比为 95.43%。与手动记录结果相比，我们提出的框架的自动 RQD 估计误差仅为 1.46%，证明了其卓越的可靠性和有效性。然后在额外的测试集上验证该框架的稳健性，证明其在岩土工程实践中广泛采用的潜力。