A Fast Circle Detection Method Based on a Tri-Class Thresholding for High Detail FPC Images

Abstract

In advanced integrated circuit packing, a very important substrate is high-density flexible printed circuit (FPC) board, which is small and foldable. The detection of the circular holes on FPCs’ surfaces through machine vision is very useful to identify root causes in FPC manufacturing process. However, it is a challenge as FPC images always contain many high details. This paper proposes a circle detection method with a modified tri-class thresholding-based contour extraction to detect and measure the circular holes on FPCs’ surfaces. It first extracts object contours by using a modified tri-class thresholding and then uses two circle validation criterions to eliminate noncircles: one is based on geometrical features and the other is based on the Helmholtz principle. Moreover, an iterative circle parameter refinement strategy is proposed. Experiments are carried out on 120 FPC images and the results indicate that the modified tri-class thresholding-based contour extraction method is more efficient than Canny detector, and the proposed circle detection method shows a better performance in terms of false detection rate, computation cost, and robustness than three other popular algorithms for detecting the circles in FPC images.