The cracks developed in the layered rock mass of grotto roofs intersect with each other,which can easily cause instability and failure of the cave rock mass.Rapid and precise fracture identification is crucial for grotto protection.To meet the need for non-contact,precise fracture measurement,this study integrates thermal infrared detection technology with an improved UNet network model to extract binary maps of roof fracture networks.Clustering algorithms are employed for segmentation and recognition,achieving a Dice coefficients of 71.63%and a detection speed of 0.84 frames/s.The method exhibits high extraction efficiency,accuracy,good applicability of thermal infrared images and resilience against artificial structure influence.Applied to the roof of Anyue Yuanjue Grotto,this method successfully identified 153 fractures and reveals dominant fracture trends at NW327° and NE55°,outperforming other measurement techniques.
关键词
石窟寺/岩体裂隙识别/深度学习/UNet网络/裂隙分组/聚类分析
Key words
grotto temple/rock mass fracture identification/deep learning/UNet network/partitioning of fracture/cluster analysis
维普
万方数据