A high precision method, based on random sampling consensus (RANSAC) and least squares support vector machine (LSSVM) regression, is advanced to improve the inspection for inertial confinement fusion (ICF) optics damage. In present study, the measurement precision of damaged areas is improved significantly, via analyzing the regression model of grey value and size. RANSAC is adopted for optimizing the training samples to avoid the effect of outliers. In addition, special experiment is proceeded to assess the effect of sample size on the measurement precision and the efficiency, and the appropriate interval of sample size is obtained as well. RANSAC-LSSVM can get optimal regression models which is adopted in different evaluative systems, by using different error evaluation functions. The results show that RANSAC-LSSVM reduces the mean relative error by 90% approximately, in measuring of damaged areas, compared with traditional pixel-level detection.