Defect identification of bare printed circuit boards based on Bayesian fusion of multi-scale features.

ABSTRACT: The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale co-occurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component

SUBMITTER: Han X 

PROVIDER: S-EPMC10909203 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature