Machine-learning-based thermal infrared recognition of fractures in grotto roofs
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.
grotto templerock mass fracture identificationdeep learningUNet networkpartitioning of fracturecluster analysis
万方数据
维普
