A Machine Vision System for Stacked Substrates Counting With a Robust Stripe Detection Algorithm

摘要

Batch measurement of sheet-like product quantity is very common in manufacturing and commercial areas. Particularly, to satisfy the growing requirement of automatic and nondestructive detection, a machine vision system for stacked substrates counting is proposed in this paper. With a brief description of the system architecture and imaging module, the challenges for real data analysis are investigated. Our main contribution is to develop a robust stripe detection algorithm by combining the merits of local template matching and global frequency domain filtering. Besides the peak-valley feature across the stack profile, the collinear prior shape along the substrates is also utilized in a morphological scheme to purify the obtained ridge-line or stripe images, which, finally, benefits the statistical counting output. It is verified in experiments using a diversity of substrate media that our developed system can achieve a high counting accuracy with the detection error not more than 0.01 as the sample thickness varies between 0.05 mm and 0.5 mm. Moreover, the proposed algorithm performs much robustly with various kinds of abnormalities and interferences like irregular piling, media distortion, and foreign contamination.