Title: Online variety recognition of auto rack girders based on combination of Fuzzy ART neural network with D-S evidence theory
Authors: Hua Wang, Jingang Gao, Shuang Zhang
Addresses: College of Mechanical Science and Engineering, Changchun Institute of Technology, Changchun 130012, China. ' College of Mechanical Science and Engineering, Changchun Institute of Technology, Changchun 130012, China. ' College of Mechanical Science and Engineering, Changchun Institute of Technology, Changchun 130012, China
Abstract: To address the difficulty of artificial recognition of hundreds of auto rack girders, this paper introduces an online automatic inspection method which synthesises machine vision, wavelet transformation theory, Fuzzy ART neural networks and D-S evidence theory on auto rack girders. First, local entropy, NMI and energy value of wavelet coefficients are used as input layers of a Fuzzy ART neural network, to gain the basic confidences of these three different characters. Next, D-S evidence theory is used to fuse the three basic confidences. Finally, total confidence in auto rack girder images, is obtained to determine a model for the inspected auto rack girders. This project of variety recognition for auto rack girders using D-S evidence theory and the Fuzzy ART neural network provides a new technology for use at home or overseas, which resolves the question of the lower recognition rate for a single character template and advances a method for multi-character fusion.
Keywords: wavelet transform; local entropy; fuzzy ART; D-S evidence theory; normalised moment of inertia; NMI; variety recognition; auto rack girders; neural networks; artificial recognition; automatic inspection; machine vision; image recognition; multi-character fusion.
International Journal of Modelling, Identification and Control, 2009 Vol.8 No.3, pp.198 - 204
Available online: 17 Nov 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article