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基于Boosting-Monodepth的管道病害深度估计与三维重建 被引量:1

Pipeline Damage Depth Estimation and 3D Reconstruction Based on Boosting-Monodepth Algorithm
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摘要 城市地下管道是城市的血脉经络,但随着排水管道的大量投入运营和使用年限增加,引发了一系列的管道病害安全隐患,如管道整体结构变形、内表面破裂和管中异物插入等问题,传统的病害图像视频采集、检测和后期病害分类甄选都是从二维视角出发,欠缺对三维空间信息(深度)的考虑。针对上述3种病害从生成深度图、由二维深度图重建三维管道病害这两方面进行研究,提出了一种基于boosting-monodepth的双重深度估计方法以提升深度图效果,最终生成画面连续一致、轮廓清晰的深度图。性能评估方面采用Abs-Rel、RMSE、SqRel、ORD和D3R等通用指标,与传统算法对比,结果显示boosting-monodepth的RMSE值降低了30%,精确度指标δ<1.25时,模型深度信息预测精确度提高了18%,此后以得到的深度图为基础重建管道病害三维点云,并在CloudCompare软件上三维可视化,最后采用随机采样一致算法测算病害深度并和实测数据对比证明其有效性和准确性。 Urban underground pipelines are the blood channels of the city.However,with the large number of drainage pipelines put into operation and their service life increasing,a series of hidden dangers of pipeline diseases have arisen,such as the overall structural deformation of the pipeline,internal surface cracking,and the insertion of foreign matters in the pipeline.Traditional disease image video capture,detection,and later disease classification and selection are conducted from a twodimensional(2D)perspective,lacking consideration of three-dimensional spatial information(depth).Aimed at the above three diseases,an experiment is conducted from generating depth maps and reconstructing three-dimensional(3D)pipeline diseases from 2D depth maps.A dual depth estimation method based on boostingmonodepth is proposed to improve the effect of depth maps,and finally a depth map with continuous and consistent images and clear outlines is generated.In terms of performance evaluation,Abs-Rel,RMSE,SqRel,ORD,D~3R and other general indexes are compared with those of the traditional algorithm,and the results show that the boosting-monodepth is reduced by 30%at RMSE and increased by 18%at a threshold ofδ<1.25.Afterwards,the 3D point cloud of pipeline disease is reconstructed based on the depth map obtained,and 3D visualization is performed on CloudCompare.Finally,the disease depth is calculated using the random sampling consistent algorithm and compared with the real measured data to prove its effectiveness and accuracy.
作者 方宏远 姜雪 王念念 胡群芳 雷建伟 王飞 赵继成 代毅 FANG Hongyuan;JIANG Xue;WANG Niannian;HU Qunfang;LEI Jianwei;WANG Fei;ZHAO Jicheng;DAI Yi(Yellow River Laboratory,Zhengzhou University,Zhengzhou 450001,China;Shanghai Institute of Disaster Prevention and Relief,Shanghai 200092,China;Key Laboratory of Urban Safety Risk Monitoring and Early Warning of the Ministry of Emergency Management,Shanghai 200092,China;Beijing Beipai Construction Co.,Ltd.,Beijing 100071,China;Shenzhen Bomingwei Technology Co.,Ltd.,Shenzhen 518000,China)
出处 《同济大学学报(自然科学版)》 EI CAS CSCD 北大核心 2023年第2期161-169,共9页 Journal of Tongji University:Natural Science
基金 国家自然科学基金(51978630) 国家重点研发计划(2022YFC3801000)。
关键词 管道病害 深度估计 三维重建 pipeline disease depth estimation three-dimensional reconstruction
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