Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/43090
標題: Automated diagnosis of sewer pipe defects based on machine learning approaches
作者: Yang, M.D.
楊明德
Su, T.C.
關鍵字: CCTV images;sewer pipe defects;diagnostic system;textural features;support vector machines;neural-network;texture segmentation;wavelet;transform;classification;model;recognition;pattern;rehabilitation;inspection
Project: Expert Systems with Applications
期刊/報告no:: Expert Systems with Applications, Volume 35, Issue 3, Page(s) 1327-1337.
摘要: 
In sewage rehabilitation planning, closed circuit television (CCTV) systems are the widely used inspection tools in assessing sewage structural conditions for non man entry pipes. Currently, the assessment of sewage structural conditions by manually interpretation oil CCTV images seems inefficient, especially for several thousands of frames in one inspection plan. Also, the assessment work significantly involves engineers' eye sight and professional experience. With a purpose of assisting general staffs in diagnosing pipe defects on CCTV inspection images, a diagnostic system by applying machine learning approaches is proposed in this paper. This research was first to use image process techniques, including wavelet transform and computation of co-occurrence matrices, for describing the textures of the pipe defects. Then, three neural network approaches, back-propagation neural network (BPN), radial basis network (RBN), and support vector machine (SVM), were adopted to classify pipe defect patterns, and their performances were compared and discussed. The diagnostic system of pipe defects was applied to a sewer system in the 9th district, Taichung City which is the largest city in middle Taiwan. The result shows that the diagnosis accuracy of 60%, derived by SVM is the best and also better than the diagnosis accuracy of 57.4%, derived by a Bayesian classifier. (C) 2007 Elsevier Ltd. All rights reserved.
URI: http://hdl.handle.net/11455/43090
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2007.08.013
Appears in Collections:土木工程學系所

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