1 July 2007 Surface detection using a phase-shift laser range finder and neural network
Laurent Gatet, Helene Tap-Beteille, Marc Lescure
Author Affiliations +
Abstract
This paper deals with a novel approach for achieving real-time surface recognition. The aim of this study is to detect different kinds of surfaces using a phase-shift range finder and a neural network (NN). The NN architecture is a multilayer perceptron with two inputs, three processing neurons in the hidden layer, and one output neuron. The first and the second inputs receive respectively the amplified and filtered photoelectric signal and the range finder output signal. The NN output is compared with threshold voltages in order to classify the tested surfaces. This recognition system has been studied with data from experimental measurements, achieved with four kinds of surfaces (a plastic surface, a glossy paper, a painted wall, and a porous surface), at a remote distance between the range finder and the target varying from 0.5 to 2 m and with a laser beam incidence angle with respect to the target varying between -π5 and π/5.
©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)
Laurent Gatet, Helene Tap-Beteille, and Marc Lescure "Surface detection using a phase-shift laser range finder and neural network," Optical Engineering 46(7), 073002 (1 July 2007). https://doi.org/10.1117/1.2757117
Published: 1 July 2007
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Sensors

Neurons

Laser range finders

Neural networks

Computer simulations

Optical engineering

Electronics

RELATED CONTENT


Back to Top