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Proceedings Paper

Neural-network-based classification of highway scenes for vehicle guidance
Author(s): Surender K. Kenue
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Paper Abstract

Current segmentation and classification algorithms are not sufficiently robust to provide reliable real-time sensor-based vehicle guidance for Intelligent Vehicle Highway Systems. An alternative technique based on neural networks was developed for scene classification as these algorithms can handle missing and fuzzy data. The back-propagation algorithm was successfully used in conjunction with new activation functions for classification of roads, lane markers, shadows, grass, and edge transitions in real-highway scenes. Extracted from 15 training images were 131 subregions of size 3 X 3 with known classes such as roads, lane markers, shadows, grass, and low and high-edge transitions. Two different neural network architectures based on image and edge data were defined. The neural network was then trained for learning the characteristics of desired classes. After convergence of the training phase was completed, the test images were correctly classified into the desired classes. The training time of 2.14 hours was significantly lower than that of days to a week, as reported by other researchers for similar applications. The processing speed and classification accuracy based on visual criteria were excellent for the test images.

Paper Details

Date Published: 14 February 1992
PDF: 11 pages
Proc. SPIE 1613, Mobile Robots VI, (14 February 1992); doi: 10.1117/12.135194
Show Author Affiliations
Surender K. Kenue, General Motors Research Labs. (United States)


Published in SPIE Proceedings Vol. 1613:
Mobile Robots VI
William J. Wolfe; Wendell H. Chun, Editor(s)

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