Cognitive Visual Inspection Service for LCD Manufacturing Industry

TL;DR

This paper introduces a robust, high-accuracy visual inspection system for LCD manufacturing that combines traditional computer vision with deep learning, enabling economical training and robustness to image quality variations.

Contribution

The paper presents a novel defect detection and classification system for LCDs, integrating deep neural networks with conventional techniques within a cognitive service architecture.

Findings

High inspection accuracy achieved on large-scale real-world LCD dataset

System demonstrates robustness to variations in image quality

Successfully deployed in an industrial LCD manufacturing line

Abstract

With the rapid growth of display devices, quality inspection via machine vision technology has become increasingly important for flat-panel displays (FPD) industry. This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry. The system is based on two cornerstones: robust/high-performance defect recognition model and cognitive visual inspection service architecture. A hybrid application of conventional computer vision technique and the latest deep convolutional neural network (DCNN) leads to an integrated defect detection, classfication and impact evaluation model that can be economically trained with only image-level class annotations to achieve a high inspection accuracy. In addition, the properly trained model is robust to the variation of the image qulity, significantly alleviating the dependency between the model prediction performance and the image aquisition environment. This in turn justifies the decoupling of the defect recognition functions from the front-end device to the back-end serivce, motivating the design and realization of the cognitive visual inspection service architecture. Empirical case study is performed on a large-scale real-world LCD dataset from a manufacturing line with different layers and products, which shows the promising utility of our system, which has been deployed in a real-world LCD manufacturing line from a major player in the world.