A Novel Feature Detection Method Using Multi-Dimensional Image Fusion for Automated Optical Inspection on Critical Dimension

This paper presents a novel approach which is based on multi-dimension image fusion to effective extraction and segmentation of edge features for accurately measuring critical dimension on objects having complicated surface patterns or random reflectance. In the approach, coarse estimation of edge points is firstly performed by using the 3D edge detector to identify correct image regions of interest (ROI) for object segmentation. 2D image processing algorithms are performed on the ROI to segment the precise object edges for critical dimension (CD) measurement. To verify the effectiveness of the strategy, the developed method has been verified through measurement of aerospace composite parts for its edge detection and critical dimension accuracy. The measurement repeatability error of this critical dimension can be kept below 1.1% of the measured CD while the standard deviation can be kept less than 0.137 mm. Experimental results have demonstrated the feasibility and applicability of the developed method.