Multi-Scale Feature Fusion for Intelligent Recognition of Tunnel Face Fractures

Accurate recognition of fractures on tunnel faces is essential for evaluating surrounding-rock integrity and ensuring excavation safety, yet it remains difficult because fracture traces are slender, irregular, discontinuous, and easily obscured by complex rock textures and illumination variability. This study proposes MF-DeepLabv3+, an enhanced DeepLabv3+-based semantic segmentation framework for tunnel-face fracture identification and geometric characterization. Unlike existing attention-based DeepLab variants that mainly enhance global feature representation, MF-DeepLabv3+ is specifically designed for thin and discontinuous tunnel-face fracture segmentation by integrating a Multi-Scale Cross Attention module for multi-receptive-field feature interaction, a Feature Smoothing Module for noise suppression and fracture-continuity enhancement, and a lightweight MobileNetV2 backbone for improved computational efficiency. A dataset of 2153 annotated images collected from the Qingdao Jiaozhou Bay Second Subsea Tunnel and the Yantai Urban Rapid Road Tunnel was established for training and evaluation. Considering the strong class imbalance between fracture and background pixels, Accuracy is reported only as an auxiliary metric, while mAP, mIoU, per-class IoU, and fracture-specific Precision, Recall, and F1-score are emphasized to provide a more reliable assessment of segmentation performance. Comparative and ablation experiments show that MF-DeepLabv3+ achieved 82.56% mAP and 62.99% mIoU, with an auxiliary Accuracy of 92.47%. Compared with the original DeepLabv3+ baseline, the proposed model achieved a substantial improvement in mAP and a modest improvement in mIoU, indicating enhanced fracture recognition capability and slightly improved region-level overlap and a moderate increase in computational cost in exchange for improved segmentation performance. Fracture grouping and post-processing were further performed using edge detection, Hough transform, connected-component analysis, and fitted-line geometry to estimate fracture length and width. The proposed method therefore enables more reliable tunnel-face fracture recognition and provides quantitative geometric information for engineering assessment and geological interpretation.