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Proceedings Paper

Video-based automatic surveillance of oil leaks in a power plant by using artificial neural networks
Author(s): T. Ogawa; Koichi Ogawa; Hidehiro Uekusa; Minoru Mukasa; J. Momii
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Paper Abstract

A new technique for the detection of oil leaks in a power plant is presented which is based on artificial neural networks. In this system the neural network was trained by feature parameters extracted from ITV images of the normal condition and oil leaks. For input data in the neural network we used four parameters calculated from two regions of interest in a subsequent image. They were the rate of variation of pixel values, variance of the pixel values, skewness of the pixel values and rate of variation of the skewness. The results showed that the accuracies of recognition were more than about 90%. The system is considered to be helpful for industrial surveillance application.

Paper Details

Date Published: 29 January 1999
PDF: 7 pages
Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); doi: 10.1117/12.339832
Show Author Affiliations
T. Ogawa, Hosei Univ. (Japan)
Koichi Ogawa, Hosei Univ. (Japan)
Hidehiro Uekusa, Fuji Electric Co. Ltd. (Japan)
Minoru Mukasa, Fuji Electric Co. Ltd. (Japan)
J. Momii, Fuji Electric Co. Ltd. (Japan)

Published in SPIE Proceedings Vol. 3584:
27th AIPR Workshop: Advances in Computer-Assisted Recognition
Robert J. Mericsko, Editor(s)

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