检索结果-南通市图书馆

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city

引用

Frontiers of Environmental Science & Engineering 2019年第2期13卷 29-41页

作者： Xiangyang Ye Jian’e Zuo Ruohan Li Yajiao Wang Lili Gan Zhonghan Yu Xiaoqing Hu State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China thunip corp. Ltd.27F/Section CS&T BuildingTsinghua S&T ParkBeijing 100084China China Water Environment Group.10F Campbell Building89th Jinbao StreetBeijing 101101China

Closed circuit television(CCTV)systems are widely used to inspect sewer pipe *** the diagnosis process,the manual diagnosis of defects is time consuming,labor intensive and error *** assist inspectors in diagnosing sewer pipe defects on CCTV inspection images,this paper presents an image recognition algorithm that applies features extraction and machine learning *** algorithm of image recognition techniques,including Hu invariant moment,texture features,lateral Fourier transform and Daubechies(DBn)wavelet transform,was used to describe the features of defects,and support vector machines were used to classify sewer pipe *** to the inspection results,seven defects were defined;the diagnostic system was applied to a sewer pipe system in a southern city of China,and 28,760 m of sewer pipes were *** results revealed that the classification accuracies of the different defects ranged from 51.6% to 99.3%.The overall accuracy reached 84.1%.The diagnosing accuracy depended on the number of the training samples,and four fitting curves were applied to fit the *** to this paper,the logarithmic fitting curve presents the highest coefficient of determination of 0.882,and more than 200 images need to be used for training samples to guarantee the accuracy higher than 85%.

关键词： Sewer pipe defects Defect diagnosing Image recognition Multi-features extraction Support vector machine

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