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Dent detection in car bodies

Authors: Tilo Lilienblum; Bernd Michaelis; Peter Albrecht; Roman Calow;

Dent detection in car bodies

Abstract

Describes a method for the automatic detection of small dents in car bodies which are not visible until the paintwork. For automatic error detection, two problems have to be solved. The accuracy of the measurement system has to be on a sufficient level and the errors have to be detected in the 3D measurement data. The measuring of the surface shape can be done by an optical 3D measurement system. This system consists of two cameras and one projector. The problem of error detection is solved by a method based on neural networks. The measurement data of one or more master workpieces is stored in the weights of a neural network. The calculation of the difference between the measurement data and the output of the neural network gives the resulting error surface. In the paper, a combination of both technologies is described. This dent detection method is illustrated by an example.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Average
Top 10%
Average
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