Applying Deep Learning to Construct a Defect Detection System for Ceramic Substrates

Abstract

Under the emerging topic of machine vision technology replacing manual examination, automatic optical inspection (AOI) technology has been adopted for the detection of defects in semi-finished/finished products and is widely used for the defect detection of printed circuit boards (PCB) in electronic industries where surface mount technology (SMT) is applied. In order to convert images from gray-scale to binary in the PCB process, a strict threshold value was set for AOI to prevent ‘escapes’, but this can lead to serious false alarm because of unwanted noises. Therefore, they tend to set up a Noise-Removal procedure after AOI, which increases the computational cost. By applying deep learning to circuit images of the ceramic substrates in AOI, this paper aimed to construct an automatic defect detection system that could also identify the categories as well as the locations of defects. This study proposed and evaluated three models with integrated structures: ResNeXt+YOLO v3, Inception v3+YOLO v3, and YOLO v3. The outcomes indicate that the defect detection system built on ResNeXt+YOLO v3 could most effectively detect standard images that had been misidentified as defects by AOI, categorize genuine defects, and find their location. The proposed method could not only increase the inspection accuracy to 99.2%, but also help decrease the cost of human resources generated by manual re-examination.