A WT-FEBFNN approach to battery defect inspection

Jing Luo; Shu-zhong Lin; Jian-yun Ni; Li-mei Song

doi:10.1117/12.836800

10 July 2009 A WT-FEBFNN approach to battery defect inspection

Jing Luo, Shu-zhong Lin, Jian-yun Ni, Li-mei Song

Proceedings Volume 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering; 74890Z (2009) https://doi.org/10.1117/12.836800
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China

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.

Citation Download Citation

Jing Luo, Shu-zhong Lin, Jian-yun Ni, and Li-mei Song "A WT-FEBFNN approach to battery defect inspection", Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890Z (10 July 2009); https://doi.org/10.1117/12.836800

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