Real-time muck analysis is of great importance for assisting tunnel boring machines (TBMs) in intelligent tunneling. Typically, muck images are characterized by low contrast, large appearance differences, and object overlap, posing a great challenge to image segmentation. In this study, a deep learning-based approach, composed of a dual UNet with multi-scale inputs and side-output (MSD-UNet) and a post-processing algorithm, was proposed to solve the automatic segmentation of muck images and estimate the size and shape of rock chips. The MSD-UNet used a dual structure with two decoders to segment the regions and boundaries of rock chips in a unified network, aiming to solve the overlapping problem of rock chips by introducing the boundary information. It also integrated a multi-scale input and side outputs to enhance low-level image features and supervise the training of early layers of the network, respectively. An integrated loss function based on generalized dice loss was developed to solve the class imbalance problem. The multi-radius erosion and seed filling algorithms were employed to further separate the connected chips in the post-processing. To evaluate the effectiveness of the method, a dataset containing various muck images collected from a TBM construction site was set up, and the MSD-UNet was trained and tested. Experimental results showed that the segmentation using the proposed approach outperformed those of using U-Net and comparable conventional methods. It achieved the highest F1-score of 0.867 and 0.640 on the region and boundary task respectively, and an average Hausdorff distance of 3.59 mm for the rock chip instance. The proposed approach can process a 2,048 × 2,048 image in about 4 s and can nearly meet the requirement of real-time TBM muck image analysis.

实时碎石分析对于协助智能隧道掘进机（TBM）至关重要。通常，渣土图像的特征在于低对比度，较大的外观差异和对象重叠，这对图像分割提出了很大的挑战。在这项研究中，提出了一种基于深度学习的方法，该方法由具有多尺度输入和边输出的双UNet（MSD-UNet）和后处理算法组成，用于解决渣土图像的自动分割和估计碎石的大小和形状。MSD-UNet使用具有两个解码器的双重结构在统一网络中分割岩屑的区域和边界，旨在通过引入边界信息来解决岩屑的重叠问题。它还集成了多尺度输入和侧面输出，以增强低级图像功能并分别监督网络早期层的训练。为解决类不平衡问题，开发了一种基于广义骰子损失的综合损失函数。采用多半径侵蚀和种子填充算法在后续处理中进一步分离连接的芯片。为了评估该方法的有效性，建立了一个包含从TBM施工现场收集的各种渣土图像的数据集，并对MSD-UNet进行了培训和测试。实验结果表明，使用所提方法进行的分割要优于使用U-Net和可比的常规方法进行的分割。它达到了最高 为解决类不平衡问题，开发了一种基于广义骰子损失的综合损失函数。采用多半径侵蚀和种子填充算法在后续处理中进一步分离连接的芯片。为了评估该方法的有效性，建立了一个包含从TBM施工现场收集的各种渣土图像的数据集，并对MSD-UNet进行了培训和测试。实验结果表明，使用所提出的方法进行的分割要优于使用U-Net和可比的常规方法进行的分割。它达到了最高 为解决类不平衡问题，开发了一种基于广义骰子损失的综合损失函数。采用多半径侵蚀和种子填充算法在后续处理中进一步分离连接的芯片。为了评估该方法的有效性，建立了一个包含从TBM施工现场收集的各种渣土图像的数据集，并对MSD-UNet进行了培训和测试。实验结果表明，使用所提方法进行的分割要优于使用U-Net和可比的常规方法进行的分割。它达到了最高 建立了一个包含从TBM施工现场收集的各种渣土图像的数据集，并对MSD-UNet进行了培训和测试。实验结果表明，使用所提方法进行的分割要优于使用U-Net和可比的常规方法进行的分割。它达到了最高 建立了一个包含从TBM施工现场收集的各种渣土图像的数据集，并对MSD-UNet进行了培训和测试。实验结果表明，使用所提方法进行的分割要优于使用U-Net和可比的常规方法进行的分割。它达到了最高F1分数在区域任务和边界任务上分别为0.867和0.640，岩屑实例的平均Hausdorff距离为3.59 mm。所提方法可以在约4 s内处理2,048×2,048图像，几乎可以满足实时TBM渣土图像分析的要求。