Scale-space effect and scale hybridization in image intelligent recognition of geological discontinuities on rock slopes  

作　　者：Mingyang Wang Enzhi Wang Xiaoli Liu Congcong Wang 

机构地区：[1]State Key Laboratory of Hydro-science and Engineering,Tsinghua University,Beijing,100084,China

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2024年第4期1315-1336,共22页岩石力学与岩土工程学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.52090081);the State Key Laboratory of Hydro-science and Hydraulic Engineering(Grant No.2021-KY-04).

摘　　要：Geological discontinuity(GD)plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses.Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data.Inspired by recent advances in computer vision,deep learning(DL)models have been widely utilized for image-based fracture identification.The multi-scale characteristics,image resolution and annotation quality of images will cause a scale-space effect(SSE)that makes features indistinguishable from noise,directly affecting the accuracy.However,this effect has not received adequate attention.Herein,we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques.Next,we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).Combining these metrics with the scale-space theory,we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition.It is found that augmenting the image's detail capacity does not always yield benefits for vision-based recognition models.In light of these observations,we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation.The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD.It also facilitates the objective understanding,description and analysis of the rock behavior and stability of slopes from the perspective of image data.

关 键 词：Image processing Geological discontinuities Deep learning MULTI-SCALE Scale-space theory Scale hybridization 

分 类 号：TU45[建筑科学—岩土工程] TP18[建筑科学—土工工程]