Empty glass bottle inspection method based on fuzzy support vector machine neural network and machine vision

doi:10.1007/s11460-010-0114-y

Front Elect Electr Eng Chin

2010, Vol. 5

Issue (4) : 430-440 https://doi.org/10.1007/s11460-010-0114-y

RESEARCH ARTICLE

Empty glass bottle inspection method based on fuzzy support vector machine neural network and machine vision

Huanjun LIU(

)

Faculty of Automation, Guangdong University of Technology, Guangzhou 510006, China

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Abstract

This paper develops a computerized empty glass bottle inspection method. Wavelet transform and morphologic methods were employed to extract features of the bottle body and the finish from images. Fuzzy support vector machine neural network was adopted as classifiers for the extracted features. Experimental results indicated that the accuracy rate can reach up to 97% by using the method developed to inspect empty glass bottles.

Keywords machine vision support vector machine (SVM) neural network (NN) morphologic method wavelet transform

Corresponding Author(s): LIU Huanjun,Email:hjliu74@163.com

Issue Date: 05 December 2010

Cite this article:

Huanjun LIU. Empty glass bottle inspection method based on fuzzy support vector machine neural network and machine vision[J]. Front Elect Electr Eng Chin, 2010, 5(4): 430-440.

URL:

https://academic.hep.com.cn/fee/EN/10.1007/s11460-010-0114-y
https://academic.hep.com.cn/fee/EN/Y2010/V5/I4/430

Fig.1 Illumination of bottle body. (a) Illumination; (b) captured image

Fig.2 Illumination of bottle finish. (a) Illumination; (b) captured image

Fig.3 Regions of interest. (a) ROI of bottle body; (b) ROI of bottle finish

Fig.4 Results of watershed transform. (a) Images of bottle wall; (b) results of modified watershed transform; (c) results of classic watershed transform

Fig.5 () of bottle finish. (a) Bottle finish; (b) ()

Fig.6 Multilevel approximations. (a) Originality; (b) level 2 approximation; (c) level 3 approximation; (d) level 4 approximation

Fig.7 Structure of FSVM

Fig.8 Flow chart of proposed algorithm

Fig.9 Fuzzy neural network

Tab.1 Comparison of accuracy of detecting glass bottle body using three methods

Tab.2 Accuracy comparison of detected glass bottle finish using three methods

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