A Genetic Algorithm Based Approach for Segmentingand Identifying Defects in Glass Bottles

This work mainly aims at designing and developing a suitable tool for identifying defects in glass bottles through visual inspection based on Segmentation algorithm. The defect identification is done in three stages. These are Image acquisition, Pre-processing and filtering and Segmentation. In the Image acquisition stage, samples of real time images are taken and are converted into 512x512 monochrome images. In the Pre- processing and filtering stage, the image acquired is passed through median filters. The Proposed filter is Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) because it produces a high value of Peak to Signal Ratio (PSNR) of 60-75db.The de-noised images is further sent to the third stage which is Segmentation. In this work, Segmentation is done using Genetic Algorithm (GA). The defects in the images are segmented and highlighted. Thus the areas of defects are spotted out. The Genetic segmentation has produced high Sensitivity, high Specificity and high Accuracy of 92%, 93% and 93% respectively. Thus the Proposed work produced effective results and hence this tool shall be useful for food processing industries for the Quality Inspection of the glass bottles.