Machine Vision Based Segmentation of the Goldplate Area from a Flexible Printed Circuit Board

Inspection of flexible printed circuit board (FPCB) is a crucial process in the manufacture industry. Besides, edge detection and extraction of the region of interest (ROI) is always a main issue. An effective segmentation method helps to reduce the influence of the irrelevant factors, thus largely augment the performance of defect inspection in the following process. In this paper, a method of segmentation of the gold finger area of FPCB was proposed to solve the problem of adhesion between gold finger and cover film. First, a Gaussian filter was applied to the grayscale image. Second, a blurry edge image was obtained by the residual image between the Gaussian image and the other after an erosion operation. Third, a binary image was created by using an adaptive threshold method, and finally, with the application of histogram statistic, the rectangle area of gold finger was extracted. Experimental results show that the proposed method accurately locate the gold finger area for our FPCB images. In addition, it takes 2.654s to extract the ROI with the adaptive threshold and 1.442s without it. By feeding it to a classifier, a total time of 3.903s was spent to analyze the possible defects, which means the algorithm is suitable for automated optical inspection (AOI) for FPCBs.

I. Introduction

Flexible printed circuit board (FPCB) is essentially a board made of polyimide or polyester, which is capable to connect electronic components. Owing to its advantages of flexibility, it has become more and more widely used in the manufacture of mobile electronics products, such as laptop, smart phone, digital camara, etc. Inspection of FPCB guarantees the product’s quality. It is a crucial process but also the largest cost in the manufacture industry [1]. In recent years, automated optical inspection (AOI) has become a popular domain of research because of its high accuracy and low cost compared to human inspection, which is currently the most common detection method[2]. For AOI, edge detection and extraction of the region of interest (ROI) has always been an important step. An effective segmentation method helps to reduce the influence of the irrelevant factors and to enhance the performance of the classifier in the following steps. Therefore, a good segmentation will largely augment the accuracy of the AOI system. Besides, for classifiers that use neural networks, images with smaller size decrease the difficulty and the cost of training a model.