China Journal of Highway and Transport

Intelligent Recognition Method of Tunnel Face Joints and Fissures Using Convolutional Neural Network

Yun-bo ZHANG, Ming-feng LEI, Yong-zhuo XIAO, Guang-hui LIU, Xing-xing DENG, Fu-yu YANG, Bao-jin LU, Chong-yang LI

China Journal of Highway and Transport, 2024, 37(7): 35-45. DOI: 10.19721/j.cnki.1001-7372.2024.07.003

检测框序号	x₁	y₁	x₂	y₂	像素量
1	2 516	529	2 641	1 019	10 892
2	3 135	1 485	3 167	1 619	1 744
3	2 074	662	2 243	1 582	18 445
4	2 875	565	3 091	1 395	21 315
5	2 389	1 536	2 418	1 634	948
6	1 881	148	2 099	418	20 001
7	2 866	1 551	3 029	1 772	7 159
8	1 648	729	1 749	863	7 553
9	2 559	1 476	2 603	1 625	2 351
10	2 456	1 246	2 501	1 387	3 617
11	2 135	492	2 301	834	13 276
12	2 611	931	2 866	1 556	16 784

Table 4 Coordinates of the Typical Tunnel Face Image Detection Frame and the Amount of Mask Pixels

Other figure/table from this article

Fig. 1 Mask R-CNN Network Structure
Fig. 2 Structure Diagram of ResNet101 and FPN
Fig. 3 Comparison of RoIAlign and RoIPool
Fig. 4 Structure Diagram of Mask R-CNN-E
Fig. 5 MBConv and SE Network Architecture Diagram
Fig. 6 Typical Tunnel Face Images
Fig. 7 Annotation Method of Images
Fig. 8 Ways of Data Augmentation
Fig. 9 Training Error Curves
Fig. 10 Training Accuracy Curves
Table 1 Model Performance Evaluation Metrics
Table 2 Single-class Evaluation Metric A₁ for Mask R-CNN-E and Mask R-CNN
Fig. 11 Recognition Effect of Joints and Fissures in the Complete Tunnel Face
Table 3 Comparison of Image Recognition of Partially Cropped Tunnel Face Under Different Algorithms
Fig. 12 Display of Different Joints and Fissures Masks Individually
Fig. 13 Mask Skeletonization
Table 5 Mask Skeleton Dimension Statistics
Fig. 14 Intelligent Identification and Measurement of Tunnel Face Joint and Fissure Compared with Actual Field Measurement Lengths