Layer Resolved Defect Detection in Transparent Plate

The loss of altitude position information of defects might result in serious over detections in the transparent plate inspection, and reduce the efficiency of the Automatic Optical Inspection (AOI). This paper proposes a layer resolved defects inspection method which uses two cameras to capture a pair of images of the defects. These two images are fused into one fusion image. A Convolution Neural Network (CNN) is trained to classify the surface where the defect is located. Then an Intersection-over-Union (IoU) based algorithm is designed to distinguish the defects for transparent plate with more than 2 surfaces. The experimental results validate the feasibility of this approach with an accuracy of 97.1% and an average detection speed of 37.45ms per defect, which is extremely helpful for industrial applications.