Crack Detection and Adaptive Threshold Segmentation in Tunnel Linings Using Faster R-CNN and WOO-DELM

Tunnel lining cracks are difficult to identify under uneven illumination, seepage marks, and complex surface textures. This study proposes a Faster R-CNN–WOO–DELM framework combining crack localization with adaptive threshold segmentation. Faster R-CNN localizes candidate crack regions, and five grayscale features are extracted from the candidate regions and valid background. A deep extreme learning machine (DELM) predicts an image-specific segmentation threshold, while the Wave Optics Optimizer (WOO) selects its regularization coefficient and stacking depth. The detection dataset comprised 2011 source images and their augmented versions. A threshold prediction subset comprising 1000 records with manually reviewed reference thresholds was constructed from the detection dataset. Faster R-CNN achieved an average precision of 0.874. On the held-out threshold test set, WOO-DELM achieved an RMSE of 0.021, a MAPE of 3.88%, an R2 of 0.954, and an a10-index of 0.945, outperforming the comparison models in threshold prediction. For pixel-level segmentation, WOO-DELM achieves image-level mean IoU and Dice/F1 values of 0.8648 and 0.9266, respectively, across 30 independent test images, with corresponding standard deviations of 0.0519 and 0.0309. Under the specified sensitivity-analysis conditions, grayscale dispersion within candidate regions had the greatest influence on the predicted segmentation threshold. The proposed framework combines crack localization with image-specific threshold selection to delineate visible tunnel lining crack traces.