Attention-Enhanced Multi-Task CNN for Subway Tunnel Lining Crack Segmentation and Defect Grading with Lightweight Deployment
Abstract
This study proposes a multi-task convolutional neural network (CNN) with a ResNet-34 backbone, CBAM attention modules, and a multi-scale fusion head for crack segmentation and defect grading in subway tunnel linings. The model integrates shared feature extraction with two task-specific heads, enabling precise crack edge segmentation and severity estimation in a unified framework. Experiments on a dataset of 12,000 RGB and multispectral images (8,400/2,400/1,200 for training/validation/testing) showed that the proposed model achieved mIoU = 91.2% ± 1.0, Dice/F1 = 93.0% ± 0.8, and mAP@0.5 = 90.7% ± 0.9 on the test set. Recognition accuracy reached 94.3%, exceeding a rule-based method (78.9%) and four deep models—U-Net, DeepLabV3+, PSPNet, and Faster R-CNN (≈88%). Evaluation replaced 'recognition accuracy' with segmentation/detection metrics: pixel-F1, mIoU, boundary F-score (BSDS), AP50-95 for instance cracks, and macro/micro-F1 for grade prediction. Per-crack type and per-grade metrics, ROC, calibration (ECE/Brier), confusion matrices, and bootstrap CIs were also reported.Average inference latency was 1.8 ± 0.2 s, with a response delay of 0.9 ± 0.1 s and an interruption rate of 2.5%, while CPU usage remained below 30% on an Intel i5 platform. Even with 10% noise, accuracy stayed at 92.1%, demonstrating strong robustness. These results confirm that the proposed framework combines accuracy, speed, and stability, supporting real-time deployment for tunnel-lining crack inspection.
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DOI: https://doi.org/10.31449/inf.v49i4.11278
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