<title>Fill-tube bore inspection with machine vision</title>

Abstract

A semi-automated technique for bore inspection of small diameter tubes is presented. The inspections are performed to insure that the bore surfaces are free of contaminants or defects. The image collectionscheme uses a borescope which is stepped along the length of the tube. An image is acquired at each step and portions from each image are combined to yield a planar image. Color analysis classifies the oxidation levels in the bore of the fill tubes. The analysis is performed by taking the image`s mean values of the red, green, and blue intensities and computing a figure of merit which is then used to estimate the relative amount of oxidation. This estimation scheme was shown to have a high level of correlation with the tube oxidation levels and the quality of the subsequent welds as determined by metallographic evaluation.Surface imperfections are detected by a series of digital filtering steps followed by a statistical analysis of the resulting binary image. The frequency parameter of the Poisson distribution for the total image and image segments are computed. A statistical significance test is performed by comparing the frequency parameter of each segment to the global statistics of the image. Fine longitudinal scratches were detected with this method.