Optimization and application of deep learning model-based subway tunnel defect detection
Aiming at the four common defects of subway tunnel,such as leakage,crack,structural plaster cracking and spalling,a defect detection method of subway tunnel based on laser radar scanning point cloud data and deep learning is studied. Firstly,the ACmix attention module is introduced into the YOLOv8 model to make the network take into account both global and local features,and improve the detection effect of small targets such as cracks and cracks. Then,the regression loss function is optimized,the convergence stability and regression accuracy are improved,and the detection error is reduced. Finally,the complete process of orthographic projection image preprocessing,batch detection and result fusion,and report generation of detection results is realized,and the defect detection of large-scale orthographic projection is efficiently realized. The experimental results show that under the condition that the IoU threshold is 0.5,the mAP of the improved YOLOv8 algorithm on the tunnel defect test set increases from 90.65％ to 91.18％,and the AP of cracks increases from 77.89％ to 78.70％. The intelligent detection of four common defects of subway tunnel based on LiDAR scanning is solved,and has been successfully applied in actual tunnel operation and maintenance engineering.
deep learningmodel optimizationinspection methodtunnel defects
NETL
NSTL
万方数据
