基于哈希算法的地下管线探地雷达图像智能识别

Intelligent recognition of underground pipeline from GPR image based on Hash algorithm

探地雷达(GPR)是一种高分辨率的地球物理无损探测方法,广泛应用于浅地表地下目标探测,在城市建设及岩土工程中有成功的应用案例.在城市生活中,地下管线承担着能源输送、信息传递等重要使命,而作为智慧城市的基础数据,地下管线空间位置信息的获取依赖于探地雷达等地球物理探测数据.传统的探地雷达地下管线图像的识别与解释,很大程度上依赖并严重受限于工作人员的专业经验,这对开展大规模的城市地下管线探测是非常不利的.本文基于这一问题,根据地下管线这类孤立目标的雷达反射波图像特征,提出了基于"以图搜图"的智能识别构想,在对原始数据进行去背景等预处理的基础上,根据哈希算法(Hash)以及约束矢量的K均值聚类分析,实现了地下管线的智能检测、图像的自动分选和识别.同时,通过提取识别区域中间道的图像亮度函数,判断管线材质.数值模拟与实测数据的应用结果表明:本算法能有效地从探地雷达剖面中识别定位管线的空间分布,并可对其材质进行判别.

Ground Penetrating Radar(GPR) is a high-resolution geophysical non-destructive detection method, which is widely used in near surface target detection, and has been successfully applied in urban construction and geotechnical engineering. In urban life, underground pipelines undertake important missions such as energy transmission and information transmission. As the basic data of smart city, the acquisition of spatial information of underground pipelines depends on geophysical detection data such as GPR. The traditional recognition and interpretation of GPR underground pipeline image mainly depends on and is seriously limited by the professional experience of the staff, which is very disadvantageous to the development of large-scale urban underground pipeline survey. To address this problem, according to the GPR reflection image characteristics of isolated targets such as underground pipelines, this paper proposes an intelligent recognition concept based on Content-Based Image Retrieval(CBIR). On the basis of background removal and other preprocessing of the original data, according to Hash algorithm and K-means clustering analysis with constraint vector, intelligent detection, automatic image sorting and recognition of underground pipeline are realized. At the same time, the material of the pipeline is judged by extracting the image brightness function of the middle trace in the recognition area. The application results of numerical simulation and field data show that the algorithm can effectively identify the spatial distribution of pipelines from GPR profiles and identify the material of pipelines.