Timely recognition of rock fragments and their morphological sizes can allow adjustment of excavation parameters during tunnel boring machine (TBM) tunnelling. Traditional manual inspection strongly relies on the experience and subjective determination of the human operators, and sieving tests cannot be conducted in real time and are energy-consuming. Rock fragments in real-world images are frequently observed against a dark background, distributed with a high size diversity, and blocked by each other. A novel deep learning-based method is proposed in this paper to achieve real-time on-site rock fragment recognition. The proposed instance segmentation model comprises two subnetworks: object detection and semantic segmentation. The object detection subnetwork is designed based on a modified single-shot detector architecture, and multilevel feature fusion, prior anchors, and self-attention modules are utilised to localise rock fragment regions. The semantic segmentation subnetwork is designed based on U-net. Down-sampling stages use the structures of the object detection subnetwork to share the extracted features of the rock fragments, and up-sampling stages employ skip connection and self-attention modules to accomplish binary segmentation in each detected bounding box. A total of 50 original images with a resolution of 4096 × 3072 were collected: 35 for training and 15 for testing. The results showed that 88.8% of the rock fragments can be recognised and that the average recall and average intersection-over-union values reach 0.87 and 0.76, respectively. Small rock fragments inevitably missed in the labelling process and extremely large ones can also be recognised. The predicted size distributions of the rock fragments fit well with the ground truth ones. Ablation experiments were conducted to further demonstrate the effectiveness of the proposed method. This study presents both visual recognition and statistical results of the size distribution of rock fragments during TBM tunnelling, which can assist in the prediction of rock properties and the adjustment of excavation parameters.

及时识别岩石碎片及其形态尺寸可以在隧道掘进机 (TBM) 隧道掘进过程中调整开挖参数。传统的人工检测严重依赖人工操作者的经验和主观判断，筛分检测不能实时进行，耗能大。现实世界图像中的岩石碎片经常在黑暗背景下观察到，分布具有很高的尺寸多样性，并且相互阻挡。本文提出了一种新的基于深度学习的方法来实现现场岩石碎片的实时识别。所提出的实例分割模型包括两个子网络：对象检测和语义分割。目标检测子网络是基于改进的单次检测器架构设计的，多级特征融合、先验锚和自注意力模块用于定位岩石碎片区域。基于U-net设计语义分割子网。下采样阶段使用目标检测子网络的结构来共享岩石碎片的提取特征，上采样阶段使用跳跃连​​接和自注意力模块在每个检测到的边界框内完成二值分割。总共收集了 50 张分辨率为 4096 × 3072 的原始图像：35 张用于训练，15 张用于测试。结果表明，88.8%的岩石碎片可以被识别，平均召回率和平均交叉联合值分别达到0.87和0.76。标记过程中不可避免地会遗漏小石块，但也可以识别出非常大的石块。岩石碎片的预测尺寸分布与地面实况非常吻合。进行了消融实验以进一步证明所提出方法的有效性。本研究提供了TBM隧道掘进过程中岩石碎片尺寸分布的视觉识别和统计结果，有助于岩石特性的预测和开挖参数的调整。