State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310027, China.
General Institute of Design and Research, China Railway Engineering Equipment Group Co., LTD, Zhengzhou, 450016, China.
Sci Rep. 2022 Feb 2;12(1):1722. doi: 10.1038/s41598-022-05727-5.
Rock mass condition assessment during tunnel excavation is a critical step for the intelligent control of tunnel boring machine (TBM). To address this and achieve automatic detection, a visual assessment system is installed to the TBM and a lager in-situ rock mass image dataset is collected from the water conveyance channel project. The rock mass condition assessment task is transformed into a fine-grain classification task. To fulfill the task, a self-convolution based attention fusion network (SAFN) is designed in this paper. The core of our method is the discovery and fusion of the object attention map within a deep neural network. The network consists of two novel modules, the self-convolution based attention extractor (SAE) module and the self-convolution based attention pooling algorithm (SAP) module. The former is designed to detect the intact rock regions generating the attention map, and the latter is designed to improve the performance of classifier by fusing the attention map that focuses on the intact rock regions. The results of SAFN are evaluated from aspects of interpretability, ablation, accuracy and cross-validation, and it outperforms state-of-the-art models in the rock mass assessment dataset. Furthermore, the dynamic filed test show that our assessment system based on the SAFN model is accurate and efficient for automated classification of rock mass.
隧道掘进过程中的岩体状况评估是 TBM（隧道掘进机）智能控制的关键步骤。为了解决这个问题并实现自动检测，在 TBM 上安装了一个可视化评估系统，并从输水通道项目中收集了更大的原位岩体图像数据集。岩体状况评估任务被转化为细粒度分类任务。为了完成这项任务，本文设计了一种基于自卷积的注意力融合网络（SAFN）。我们方法的核心是在深层神经网络中发现和融合对象注意力图。该网络由两个新模块组成，即基于自卷积的注意力提取器（SAE）模块和基于自卷积的注意力池化算法（SAP）模块。前者用于检测生成注意力图的完整岩石区域，后者用于通过融合关注完整岩石区域的注意力图来提高分类器的性能。从可解释性、消融、准确性和交叉验证等方面对 SAFN 的结果进行了评估，它在岩体评估数据集上优于最先进的模型。此外，动态现场测试表明，我们基于 SAFN 模型的评估系统能够准确、高效地对岩体进行自动分类。
