Abstract
In this paper, a system based on Artificial Neural Network (ANN) for classifying automobile parts with different shapes is presented. The system gets the original information from an image sensor, classifies two sorts of automobile parts with different shapes after processing these images. The classifier designed in this paper adopts the ANN with an improved BP algorithm. The perimeter, acreage and the degree of the decentralization of the automobile parts in the intensity image are taken as the input feature vectors. For the No.1 part and No.2 part, the experimental result indicates that the recognition accuracy rate can reach up to 99% in this system and the productivity will be improved obviously comparing with the checking result in the off-line system manually.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, J., Zhao, G., Kong, L. (2006). An ANN-Based Classification System for Automobile Parts with Different Shapes. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence. ICIC 2006. Lecture Notes in Computer Science(), vol 4114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37275-2_148
Download citation
DOI: https://doi.org/10.1007/978-3-540-37275-2_148
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37274-5
Online ISBN: 978-3-540-37275-2
eBook Packages: Computer ScienceComputer Science (R0)