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Proceedings Paper

A WT-FEBFNN approach to battery defect inspection
Author(s): Jing Luo; Shu-zhong Lin; Jian-yun Ni; Li-mei Song
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Paper Abstract

Aiming at the change of battery location, environment light or camera location in Li/MnO2 automatic inspection process, a novel WT-FEBFNN (Wavelet Transform Fuzzy Ellipsoidal Basis Function Neural Network) approach to battery defect inspection is proposed. Firstly, WT is applied on original battery image, and low-frequency signal and de-noised signal is obtained, respectively, by setting different thresholds on different scale WT decomposition. Secondly, signal only containing defect (nick) is obtained by subtracting low-frequency signal from de-noised signal. Finally, model of FEBFNN is established and defect recognition is accomplished on 1000 battery images. Experiments have shown the proposed algorithm had a better robustness to the change of battery location, or environment light or camera location than multilayer perception(MLP), and shown that the reason for the high recognition accuracy in battery defect inspection is due to the information contents of the features as well as to proper classifier.

Paper Details

Date Published: 10 July 2009
PDF: 6 pages
Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890Z (10 July 2009); doi: 10.1117/12.836800
Show Author Affiliations
Jing Luo, Tianjin Polytechnic Univ. (China)
Shu-zhong Lin, Tianjin Polytechnic Univ. (China)
Jian-yun Ni, Tianjin Univ. of Technology (China)
Li-mei Song, Tianjin Polytechnic Univ. (China)


Published in SPIE Proceedings Vol. 7489:
PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering
Honghua Tan; Qi Luo, Editor(s)

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