基于YOLOv11-CL和GPU并行RTM的城市道路路基隐蔽病害快速识别定位OA

In response to the problems of limited accuracy in detecting and identifying various underground hidden defects in urban road using ground penetrating radar(GPR)and the difficulty in accurately characteriz-ing the location and morphology of these defects,a rapid identification and location technology for hidden de-fects based on YOLOv11-CGAFusion-LSDECD(YOLOv11-CL)and reverse time migration(RTM)was pro-posed.First,an intelligent identification model for void,loose and pipelines based on the improved YO-LOv11-CL was constructed to achieve accurate identification of various hidden defects in complex urban road subgrades.Then,an RTM imaging location algorithm based on a GPU-accelerated parallel symplectic method was developed to achieve rapid and accurate defects location after identification.Finally,the accuracy and reli-ability of the technology were verified through numerical simulation and engineering application.The results show that the average identification accuracy of this technology for various underground hidden defects ex-ceeds 88%,and the location error is controlled within 5%.This technology provides robust technical support for the rapid and accurate detection and diagnosis of underground hidden defects in urban road.