Defect inspection system of nuclear fuel pellet end faces based on machine vision

Abstract

ABSTRACTIn order to effectively conduct the defect detection of nuclear fuel pellets end face and avoid the leakage of nuclear radiation, a defect detection system for the nuclear fuel pellets end face based on machine vision is proposed. Firstly, aiming at the complexity of the defect detection of nuclear fuel pellets, a set of image acquisition system lighted by left-right symmetric grating is designed. Then, after fusing the images of left-right structured light those cross points are extracted which classified based on the Gaussian mixture model (GMM). Finally, a series of morphological operations such as dilation operation are conducted for the classified points to obtain the defect area of nuclear fuel pellets end face. The experimental results show that this method reduces the influence of complex characteristics of form, texture, and color of the sample end face on the defect detection and relatively good detection results are gained for various defects with 99.5% accuracy. It takes less than 0.4 s to fully meet the requirements of industrial automation testing.