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Visual positioning plays a pivotal role in enabling robotic disc cutter replacement for the shield machine. However, underground operational challenges—including low illumination, high dust concentrations, and irregular sand deposition on the surface of the disc cutter and its holder—severely compromise recognition accuracy. To address this, we propose a multi-mechanism enhanced UNet model for robust segmentation of the disc cutter holder under heterogeneous surface conditions. Experimental comparisons with mainstream semantic segmentation models demonstrate that the Res-UNet achieves superior training efficiency and segmentation accuracy. Ablation studies further reveal optimal performance when utilizing a hybrid loss function (dice loss + cross-entropy loss) paired with the Adam optimizer. By integrating attention mechanisms, we develop the Res-UNet-CA architecture, which achieves state-of-the-art metrics on independent test sets: accuracy (99.45%), precision (98.9%), recall (99.11%), F1-score (99%), and mIoU (98.63%). The Res-UNet-CA model significantly outperforms other semantic segmentation models in prediction quality, offering an innovative solution for shield machine disc cutter holder detection.

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.