Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection

Abstract

Due to the periodicity of circuit boards, the registration algorithm based on keypoints is less robust in circuit board detection and is prone to misregistration problems. In this paper, the binary neighborhood coordinate descriptor (BNCD) is proposed and applied to circuit board image registration. The BNCD consists of three parts: neighborhood description, coordinate description, and brightness description. The neighborhood description contains the grayscale information of the neighborhood, which is the main part of BNCD. The coordinate description introduces the actual position of the keypoints in the image, which solves the problem of inter-period matching of keypoints. The brightness description introduces the concept of bright and dark points, which improves the distinguishability of BNCD and reduces the calculation amount of matching. Experimental results show that in circuit board image registration, the matching precision rate and recall rate of BNCD is better than that of classic algorithms such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF), and the calculation of descriptors takes less time.