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.