Target classification with simple infrared sensors using artificial neural networks

Date
2008-10
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Source Title
23rd International Symposium on Computer and Information Sciences, ISCIS 2008
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Publisher
IEEE
Volume
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Pages
1 - 6
Language
English
Type
Conference Paper
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Abstract

This study investigates the use of low-cost infrared (IR) sensors for the determination of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders using artificial neural networks (ANNs). The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way which cannot be represented by a simple analytical relationship, therefore complicating the localization and classification process. We propose the use of angular intensity scans and feature vectors obtained by modeling of angular intensity scans and present two different neural network based approaches in order to classify the geometry and/or the surface type of the targets. In the first case, where planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material are differentiated, an average correct classification rate of 78% of both geometry and surface over all target types is achieved. In the second case, where planes, 90° edges, and cylinders covered with different surface materials are differentiated, an average correct classification rate of 99.5% is achieved. The method demonstrated shows that ANNs can be used to extract substantially more information than IR sensors are commonly employed for. © 2008 IEEE.

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Keywords
Alumina, Backpropagation, Computational geometry, Cylinders (shapes), Geometry, Information science, Infrared detectors, Packaging materials, Sensor networks, Sensors, Surface properties, Surfaces, Targets, Trace analysis, Classification processes, Classification rates, Feature vector (FV), Infrared (IR) sensors, Intensity measurements, Styrofoam packaging, Surface materials, Surface type, Target classification, Neural networks
Citation
Published Version (Please cite this version)