Automated sewer inspection using image processing and a neural classifier

Olga Duran; Kaspar Althoefer; Lakmal D. Seneviratne

Automated sewer inspection using image processing and a neural classifier

Olga Duran, Kaspar Althoefer, Lakmal D. Seneviratne

Mechanical & Nuclear Engineering

King's College London

Research output: Contribution to conference › Paper › peer-review

9 Scopus citations

Abstract

The focus of the research presented here is on the automated assessment of sewer pipe conditions using a laser-based sensor. The proposed method involves image and data processing algorithms categorising signals acquired from the internal pipe surface. Fault identification is carried out using a neural network. Experimental results are presented.

Original language	British English
Pages	1126-1131
Number of pages	6
State	Published - 2002
Event	2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: 12 May 2002 → 17 May 2002

Conference

Conference	2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/Territory	United States
City	Honolulu, HI
Period	12/05/02 → 17/05/02

Cite this

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title = "Automated sewer inspection using image processing and a neural classifier",

abstract = "The focus of the research presented here is on the automated assessment of sewer pipe conditions using a laser-based sensor. The proposed method involves image and data processing algorithms categorising signals acquired from the internal pipe surface. Fault identification is carried out using a neural network. Experimental results are presented.",

author = "Olga Duran and Kaspar Althoefer and Seneviratne, {Lakmal D.}",

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T1 - Automated sewer inspection using image processing and a neural classifier

AU - Duran, Olga

AU - Althoefer, Kaspar

AU - Seneviratne, Lakmal D.

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N2 - The focus of the research presented here is on the automated assessment of sewer pipe conditions using a laser-based sensor. The proposed method involves image and data processing algorithms categorising signals acquired from the internal pipe surface. Fault identification is carried out using a neural network. Experimental results are presented.

AB - The focus of the research presented here is on the automated assessment of sewer pipe conditions using a laser-based sensor. The proposed method involves image and data processing algorithms categorising signals acquired from the internal pipe surface. Fault identification is carried out using a neural network. Experimental results are presented.

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M3 - Paper

AN - SCOPUS:0036079766

SP - 1126

EP - 1131

T2 - 2002 International Joint Conference on Neural Networks (IJCNN '02)

Y2 - 12 May 2002 through 17 May 2002

ER -

Automated sewer inspection using image processing and a neural classifier

Abstract

Conference

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