Inspection of improved ghost imaging for hight-light LED with different factory quality

Abstract

High brightness and high reflective properties are two important challenges for machine vision inspection. We propose a new method for nonlinear feature extraction and inspection of visual weak signals from highlight-emitting rather than “interference” bright objects. Based on the ghost image principle, the highlight characteristics of the light source become a useful characteristic. Under the guidance of ghost image theory, optimized design of ghost lens group, visual inspection of bright objects for the purpose of obtaining ghost images with high signal-to-noise ratio. We improved the high-brightness LED appearance quality inspection experiment of ghost image. Results show that the inspection method of improved ghost image of highlighted objects combined with deep learning has a good detection result. The method has an accuracy rate of 90.1%. In the early stages of the visual inspection process, there is great potential in applying the theory and method of ghost image to detect objects with high brightness and high reflective properties.