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

Fig. 5 MBConv and SE Network Architecture Diagram

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