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

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.

Key words： machine vision; support vector machine (SVM); neural network (NN); morphologic method; wavelet transform

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. U0735003, 60604006), the Ph.D. Programs Foundation of Ministry of Education of China (20070562005), and the National Natural Science Foundation of Guangdong province of China (Nos. 07117423, 6021452).

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