Study of auto rack girder detection system

Author

Wang, Hua ; Gao, Jingang ; Zhang, Shuang

Author_Institution

Coll. of Mech. Sci. & Eng., Changchun Inst. of Technol., Changchun, China

Volume

2

fYear

2010

fDate

24-26 Aug. 2010

Firstpage

547

Lastpage

550

Abstract

On account of out-of-date and low efficiency detection mode for large dimension auto rack girder, it is presented that is the Machine Vision on-line detection system for auto rack girder Based on RBF and linear structured light. It is corrected according to the error of RBF neural network for camera lens. Taken advantage of RBF characteristic that approximating high order input and output nonlinear system, camera image error correction high order distortion model is constructed and the precision of camera detection is raised. This correction method is used in China FAW Group Corporation for auto rack girder detection system. Automatic non-contact detection is firstly realized in China for auto rack girder holes diameter, position and number. After tested at production field, the precision of detection diameter is 0.1mm, the precision of detection position is °0.3mm, and the detection time is less than 1.5 minutes. It is proved in practice that the method of linear array CCD camera lens distortion correction based on RBF neural network is feasible.

Keywords

beams (structures); computer vision; image recognition; nonlinear distortion; object detection; radial basis function networks; structural engineering computing; China FAW Group Corporation; RBF neural network; auto rack girder detection system; camera image; image detection; image distortion; image error correction; machine vision; nonlinear system; Artificial neural networks; Charge coupled devices; Coordinate measuring machines; RBF neural network; auto rack girder; linear array CCD; linear structured light;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer, Mechatronics, Control and Electronic Engineering (CMCE), 2010 International Conference on

Conference_Location

Changchun

Print_ISBN

978-1-4244-7957-3

Type

conf

DOI

10.1109/CMCE.2010.5610304

Filename

5610304