Title :
Automated sewer inspection using image processing and a neural classifier
Author :
Duran, Olga ; Althoefer, Kaspar ; Seneviratne, Lakmal D.
Author_Institution :
Dept. of Mech. Eng., King´´s Coll., London, UK
fDate :
6/24/1905 12:00:00 AM
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
Keywords :
automatic optical inspection; civil engineering computing; fault location; image classification; laser beam applications; neural nets; waste disposal; automated sewer inspection; data processing; fault identification; image processing; internal pipe surface; laser-based sensor; neural classifier; neural network; sewer pipe condition assessment; Cameras; Circuit faults; Data processing; Image processing; Image segmentation; Inspection; Intelligent sensors; Mechanical engineering; Neural networks; Optical sensors;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007652
